{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第一部分：获取生猪价格序列，对数据进行分类整理"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① **数据读入**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据完整，可以使用~！\n"
     ]
    }
   ],
   "source": [
    "# 1、读取数据\n",
    "rawFile = pd.read_csv('生猪月度价格历史统计表.csv', header=1, encoding='utf-8')\n",
    "# 2、数据空值处理(数据清洁将省略；如果有控制提示用户重新更改数据)\n",
    "flag = True\n",
    "for i in range(rawFile.shape[1]):\n",
    "    if(rawFile.iloc[:, i].isna().sum()):\n",
    "        print('第'+ str(i) +'列日期有空值~！请检测并确认格式后，重新运行此行~！')\n",
    "        flag = False\n",
    "        break\n",
    "    elif(rawFile.iloc[:, i].isnull().sum()):\n",
    "        print('第'+ str(i) +'列价格有null数据~！请检测并确认格式后，重新运行此行~！')\n",
    "        flag = False\n",
    "        break\n",
    "if flag:\n",
    "    print('数据完整，可以使用~！')\n",
    "    # 3、将数据转换为list形式，第一组为日期列表，第二组为价格列表\n",
    "    dateData = rawFile.iloc[:, 0].tolist()\n",
    "    priceData = rawFile.iloc[:, 1].round(4).tolist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "② **分解后的数据样式** "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-> 日期数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[200607, 200608, 200609, 200610, 200611, 200612, 200701, 200702, 200703, 200704, 200705, 200706, 200707, 200708, 200709, 200710, 200711, 200712, 200801, 200802, 200803, 200804, 200805, 200806, 200807, 200808, 200809, 200810, 200811, 200812, 200901, 200902, 200903, 200904, 200905, 200906, 200907, 200908, 200909, 200910, 200911, 200912, 201001, 201002, 201003, 201004, 201005, 201006, 201007, 201008, 201009, 201010, 201011, 201012, 201101, 201102, 201103, 201104, 201105, 201106, 201107, 201108, 201109, 201110, 201111, 201112, 201201, 201202, 201203, 201204, 201205, 201206, 201207, 201208, 201209, 201210, 201211, 201212, 201301, 201302, 201303, 201304, 201305, 201306, 201307, 201308, 201309, 201310, 201311, 201312, 201401, 201402, 201403, 201404, 201405, 201406, 201407, 201408, 201409, 201410, 201411, 201412, 201501, 201502, 201503, 201504, 201505, 201506, 201507, 201508, 201509, 201510, 201511, 201512, 201601, 201602, 201603, 201604, 201605, 201606, 201607, 201608, 201609, 201610, 201611, 201612, 201701, 201702, 201703, 201704, 201705, 201706, 201707, 201708, 201709, 201710, 201711, 201712, 201801, 201802, 201803, 201804, 201805, 201806, 201807, 201808, 201809, 201810, 201811, 201812, 201901, 201902, 201903, 201904, 201905, 201906, 201907, 201908, 201909, 201910, 201911, 201912, 202001, 202002, 202003, 202004, 202005, 202006, 202007, 202008, 202009, 202010, 202011, 202012, 202101, 202102, 202103, 202104, 202105, 202106, 202107, 202108, 202109, 202110, 202111]\n"
     ]
    }
   ],
   "source": [
    "print(dateData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-> 价格数组"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[7.26, 7.61, 8.424, 8.184, 9.19, 9.984, 10.02, 9.106, 8.92, 9.779, 12.037, 12.639, 14.929, 13.757, 12.96, 13.203, 15.145, 16.335, 16.819, 17.309, 17.397, 16.805, 15.84, 15.55, 14.68, 14.1, 12.15, 11.83, 12.02, 13.55, 13.386, 11.58, 10.66, 10.08, 9.59, 10.58, 11.29, 12.15, 11.92, 11.37, 12.02, 12.78, 11.64, 10.35, 9.63, 10.01, 9.7, 10.37, 12.84, 12.68, 12.79, 13.44, 14.29, 13.61, 14.06, 15.04, 15.01, 15.14, 16.89, 19.61, 19.29, 19.68, 19.15, 17.49, 16.56, 17.25, 17.56, 16.08, 14.77, 14.41, 13.8, 14.11, 13.88, 14.64, 14.74, 14.46, 15.31, 16.46, 17.18, 14.7, 12.77, 12.32, 13.91, 14.4, 15.09, 16.05, 15.87, 15.55, 15.77, 15.38, 12.35, 11.92, 10.77, 10.55, 13.37, 13.01, 13.97, 15.35, 14.52, 13.78, 13.85, 13.32, 12.62, 11.85, 12.24, 13.6, 14.54, 15.65, 18.25, 18.42, 17.31, 16.42, 16.5, 16.55, 18.1, 18.14, 19.75, 20.41, 21.12, 19.91, 18.22, 18.5, 17.16, 16.23, 16.92, 17.42, 18.14, 17.02, 15.89, 14.95, 13.61, 13.94, 13.93, 14.64, 14.4, 14.22, 14.29, 14.92, 14.91, 13.43, 10.49, 10.1, 10.63, 11.56, 13.17, 13.48, 14.01, 13.4, 13.24, 13.05, 11.39, 12.47, 15.14, 15.04, 15.38, 17.74, 19.24, 26.48, 28.53, 40.29, 32.08, 33.55, 36.64, 37.2, 35.03, 33.09, 29.23, 34.53, 37.36, 37.43, 33.74, 28.86, 29.82, 33.94, 34.64, 27.86, 26.69, 23.0, 18.56, 12.73, 15.61, 14.4, 12.2, 15.95, 17.15]\n"
     ]
    }
   ],
   "source": [
    "print(priceData)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第二部分：周期性分析  \n",
    "* \"猪周期\"，根本就是利润驱动的生猪供给周期。猪价是一种典型的周期性变化类产品，一轮大的周期大概持续4年左右，且在一年中猪价也是根据季节性不同而具有较明显的变化趋势。  \n",
    "* 在不考虑其他外在因素和系统性风险的前提下，其猪价主要受到生猪攻击决定，而后者主要受养殖利润驱动。  \n",
    "* 猪价的走势一种典型的\"非线性时间序列\"的数据，这种时间序列的数据具有典型的周期性，对数据按照月份进行切割并罗列，能够较明显的看出他们的这种周期特性。这里所说的周期并不是线性的周期，其相似性指的是具有更好的相关性分析结果。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "① **历史月份切割**  \n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-> 根据所需样本数据，按照月份进行切割，这里给定切分月长度，比如：6月份作为起始点，切分月长度为12的话，那就会得到：200607~200706,200707~200806,...,202007~202106...。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "dict_keys(['12月份日期开始+24个月'])\n",
      "dict_keys(['12月份价格开始+24个月'])\n"
     ]
    }
   ],
   "source": [
    "SIZE = 24   # 月份切割尺寸\n",
    "count = 0\n",
    "tempMonthList = []\n",
    "monthList = []\n",
    "tempPriceList = []\n",
    "priceList = []\n",
    "\n",
    "dataDict = {}\n",
    "priceDict = {}\n",
    "\n",
    "# 序列整理\n",
    "for i in range(len(dateData)-1, -1, -1):\n",
    "    tempMonthList.append(dateData[i])  # 日期序列\n",
    "    tempPriceList.append(priceData[i]) # 价格序列\n",
    "    count += 1\n",
    "    if len(tempMonthList) == SIZE:\n",
    "        # print(list(reversed(tempMonthList)))\n",
    "        # 日期序列\n",
    "        monthList.append(list(reversed(tempMonthList)))\n",
    "        tempMonthList = []\n",
    "        # 价格序列\n",
    "        priceList.append(list(reversed(tempPriceList)))\n",
    "        tempPriceList = []        \n",
    "        count = 0\n",
    "\n",
    "temp01 = []\n",
    "temp02 = []\n",
    "# 按照月份切割\n",
    "for m in ['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12']:\n",
    "    for i in range(len(monthList)):\n",
    "        # 挑选数据\n",
    "        if str(monthList[i][0])[4::] == m:\n",
    "            temp01.append(monthList[i]) \n",
    "            temp02.append(priceList[i])\n",
    "    if len(temp01) != 0:\n",
    "        # 合并到字典\n",
    "        dataDict[m + \"月份日期开始+\" + str(SIZE) + \"个月\"] = temp01\n",
    "        priceDict[m + \"月份价格开始+\" + str(SIZE) + \"个月\"] = temp02\n",
    "        temp01 = []\n",
    "        temp02 = []\n",
    "\n",
    "print(dataDict.keys())\n",
    "print(priceDict.keys())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print(dataDict['03月份日期数据'])\n",
    "# for i in dataDict['06月份日期数据']:\n",
    "#     print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# print(priceDict['03月份价格数据'])\n",
    "# for i in priceDict['03月份价格数据']:\n",
    "#     print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "② **月份数据展示**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.pyplot import MultipleLocator\n",
    "# 支持中文\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 用来正常显示中文标签\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 用来正常显示负号"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12月份日期开始+24个月 12月份价格开始+24个月\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1728x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(len(dataDict.keys())):\n",
    "    # 循环显示图形\n",
    "    dateKey = list(dataDict.keys())[i]\n",
    "    priceKey = list(priceDict.keys())[i]\n",
    "    \n",
    "    # 标题内容\n",
    "    plt.figure(i, figsize=(len(priceDict[priceKey][0]), 5))\n",
    "    plt.title(dateKey[0:4])\n",
    "    plt.tight_layout()\n",
    "    # x轴\n",
    "#     x = range(0, len(priceDict[priceKey][0]) -1)\n",
    "    # x轴刻度\n",
    "    ax = plt.gca()\n",
    "    ax.set_xlim([0, len(priceDict[priceKey][0])-1])\n",
    "    ax.xaxis.set_major_locator(MultipleLocator(1))  # 间距\n",
    "    print(dateKey, priceKey)\n",
    "    for j in priceDict[priceKey]:\n",
    "        plt.xlabel(dateKey)\n",
    "        plt.ylabel(priceKey)\n",
    "        plt.plot(j, marker=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "③ **月份相关性分析**  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "12月份日期开始+24个月 12月份价格开始+24个月\n",
      "[33.55, 36.64, 37.2, 35.03, 33.09, 29.23, 34.53, 37.36, 37.43, 33.74, 28.86, 29.82, 33.94, 34.64, 27.86, 26.69, 23.0, 18.56, 12.73, 15.61, 14.4, 12.2, 15.95, 17.15]\n",
      "[14.92, 14.91, 13.43, 10.49, 10.1, 10.63, 11.56, 13.17, 13.48, 14.01, 13.4, 13.24, 13.05, 11.39, 12.47, 15.14, 15.04, 15.38, 17.74, 19.24, 26.48, 28.53, 40.29, 32.08]\n",
      "[16.55, 18.1, 18.14, 19.75, 20.41, 21.12, 19.91, 18.22, 18.5, 17.16, 16.23, 16.92, 17.42, 18.14, 17.02, 15.89, 14.95, 13.61, 13.94, 13.93, 14.64, 14.4, 14.22, 14.29]\n",
      "[15.38, 12.35, 11.92, 10.77, 10.55, 13.37, 13.01, 13.97, 15.35, 14.52, 13.78, 13.85, 13.32, 12.62, 11.85, 12.24, 13.6, 14.54, 15.65, 18.25, 18.42, 17.31, 16.42, 16.5]\n",
      "[17.25, 17.56, 16.08, 14.77, 14.41, 13.8, 14.11, 13.88, 14.64, 14.74, 14.46, 15.31, 16.46, 17.18, 14.7, 12.77, 12.32, 13.91, 14.4, 15.09, 16.05, 15.87, 15.55, 15.77]\n",
      "[12.78, 11.64, 10.35, 9.63, 10.01, 9.7, 10.37, 12.84, 12.68, 12.79, 13.44, 14.29, 13.61, 14.06, 15.04, 15.01, 15.14, 16.89, 19.61, 19.29, 19.68, 19.15, 17.49, 16.56]\n",
      "[16.335, 16.819, 17.309, 17.397, 16.805, 15.84, 15.55, 14.68, 14.1, 12.15, 11.83, 12.02, 13.55, 13.386, 11.58, 10.66, 10.08, 9.59, 10.58, 11.29, 12.15, 11.92, 11.37, 12.02]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(dataDict.keys())):\n",
    "    # 循环显示图形\n",
    "    dateKey = list(dataDict.keys())[i]\n",
    "    priceKey = list(priceDict.keys())[i]\n",
    "    print(dateKey, priceKey)\n",
    "    for j in priceDict[priceKey]:\n",
    "        print(j)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0组</th>\n",
       "      <th>1组</th>\n",
       "      <th>2组</th>\n",
       "      <th>3组</th>\n",
       "      <th>4组</th>\n",
       "      <th>5组</th>\n",
       "      <th>6组</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>33.55</td>\n",
       "      <td>14.92</td>\n",
       "      <td>16.55</td>\n",
       "      <td>15.38</td>\n",
       "      <td>17.25</td>\n",
       "      <td>12.78</td>\n",
       "      <td>16.335</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>36.64</td>\n",
       "      <td>14.91</td>\n",
       "      <td>18.10</td>\n",
       "      <td>12.35</td>\n",
       "      <td>17.56</td>\n",
       "      <td>11.64</td>\n",
       "      <td>16.819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>37.20</td>\n",
       "      <td>13.43</td>\n",
       "      <td>18.14</td>\n",
       "      <td>11.92</td>\n",
       "      <td>16.08</td>\n",
       "      <td>10.35</td>\n",
       "      <td>17.309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>35.03</td>\n",
       "      <td>10.49</td>\n",
       "      <td>19.75</td>\n",
       "      <td>10.77</td>\n",
       "      <td>14.77</td>\n",
       "      <td>9.63</td>\n",
       "      <td>17.397</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>33.09</td>\n",
       "      <td>10.10</td>\n",
       "      <td>20.41</td>\n",
       "      <td>10.55</td>\n",
       "      <td>14.41</td>\n",
       "      <td>10.01</td>\n",
       "      <td>16.805</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>29.23</td>\n",
       "      <td>10.63</td>\n",
       "      <td>21.12</td>\n",
       "      <td>13.37</td>\n",
       "      <td>13.80</td>\n",
       "      <td>9.70</td>\n",
       "      <td>15.840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>34.53</td>\n",
       "      <td>11.56</td>\n",
       "      <td>19.91</td>\n",
       "      <td>13.01</td>\n",
       "      <td>14.11</td>\n",
       "      <td>10.37</td>\n",
       "      <td>15.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>37.36</td>\n",
       "      <td>13.17</td>\n",
       "      <td>18.22</td>\n",
       "      <td>13.97</td>\n",
       "      <td>13.88</td>\n",
       "      <td>12.84</td>\n",
       "      <td>14.680</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>37.43</td>\n",
       "      <td>13.48</td>\n",
       "      <td>18.50</td>\n",
       "      <td>15.35</td>\n",
       "      <td>14.64</td>\n",
       "      <td>12.68</td>\n",
       "      <td>14.100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>33.74</td>\n",
       "      <td>14.01</td>\n",
       "      <td>17.16</td>\n",
       "      <td>14.52</td>\n",
       "      <td>14.74</td>\n",
       "      <td>12.79</td>\n",
       "      <td>12.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>28.86</td>\n",
       "      <td>13.40</td>\n",
       "      <td>16.23</td>\n",
       "      <td>13.78</td>\n",
       "      <td>14.46</td>\n",
       "      <td>13.44</td>\n",
       "      <td>11.830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>29.82</td>\n",
       "      <td>13.24</td>\n",
       "      <td>16.92</td>\n",
       "      <td>13.85</td>\n",
       "      <td>15.31</td>\n",
       "      <td>14.29</td>\n",
       "      <td>12.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>33.94</td>\n",
       "      <td>13.05</td>\n",
       "      <td>17.42</td>\n",
       "      <td>13.32</td>\n",
       "      <td>16.46</td>\n",
       "      <td>13.61</td>\n",
       "      <td>13.550</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>34.64</td>\n",
       "      <td>11.39</td>\n",
       "      <td>18.14</td>\n",
       "      <td>12.62</td>\n",
       "      <td>17.18</td>\n",
       "      <td>14.06</td>\n",
       "      <td>13.386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>27.86</td>\n",
       "      <td>12.47</td>\n",
       "      <td>17.02</td>\n",
       "      <td>11.85</td>\n",
       "      <td>14.70</td>\n",
       "      <td>15.04</td>\n",
       "      <td>11.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>26.69</td>\n",
       "      <td>15.14</td>\n",
       "      <td>15.89</td>\n",
       "      <td>12.24</td>\n",
       "      <td>12.77</td>\n",
       "      <td>15.01</td>\n",
       "      <td>10.660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>23.00</td>\n",
       "      <td>15.04</td>\n",
       "      <td>14.95</td>\n",
       "      <td>13.60</td>\n",
       "      <td>12.32</td>\n",
       "      <td>15.14</td>\n",
       "      <td>10.080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18.56</td>\n",
       "      <td>15.38</td>\n",
       "      <td>13.61</td>\n",
       "      <td>14.54</td>\n",
       "      <td>13.91</td>\n",
       "      <td>16.89</td>\n",
       "      <td>9.590</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>12.73</td>\n",
       "      <td>17.74</td>\n",
       "      <td>13.94</td>\n",
       "      <td>15.65</td>\n",
       "      <td>14.40</td>\n",
       "      <td>19.61</td>\n",
       "      <td>10.580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>15.61</td>\n",
       "      <td>19.24</td>\n",
       "      <td>13.93</td>\n",
       "      <td>18.25</td>\n",
       "      <td>15.09</td>\n",
       "      <td>19.29</td>\n",
       "      <td>11.290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>14.40</td>\n",
       "      <td>26.48</td>\n",
       "      <td>14.64</td>\n",
       "      <td>18.42</td>\n",
       "      <td>16.05</td>\n",
       "      <td>19.68</td>\n",
       "      <td>12.150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>12.20</td>\n",
       "      <td>28.53</td>\n",
       "      <td>14.40</td>\n",
       "      <td>17.31</td>\n",
       "      <td>15.87</td>\n",
       "      <td>19.15</td>\n",
       "      <td>11.920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>15.95</td>\n",
       "      <td>40.29</td>\n",
       "      <td>14.22</td>\n",
       "      <td>16.42</td>\n",
       "      <td>15.55</td>\n",
       "      <td>17.49</td>\n",
       "      <td>11.370</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>17.15</td>\n",
       "      <td>32.08</td>\n",
       "      <td>14.29</td>\n",
       "      <td>16.50</td>\n",
       "      <td>15.77</td>\n",
       "      <td>16.56</td>\n",
       "      <td>12.020</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       0组     1组     2组     3组     4组     5组      6组\n",
       "0   33.55  14.92  16.55  15.38  17.25  12.78  16.335\n",
       "1   36.64  14.91  18.10  12.35  17.56  11.64  16.819\n",
       "2   37.20  13.43  18.14  11.92  16.08  10.35  17.309\n",
       "3   35.03  10.49  19.75  10.77  14.77   9.63  17.397\n",
       "4   33.09  10.10  20.41  10.55  14.41  10.01  16.805\n",
       "5   29.23  10.63  21.12  13.37  13.80   9.70  15.840\n",
       "6   34.53  11.56  19.91  13.01  14.11  10.37  15.550\n",
       "7   37.36  13.17  18.22  13.97  13.88  12.84  14.680\n",
       "8   37.43  13.48  18.50  15.35  14.64  12.68  14.100\n",
       "9   33.74  14.01  17.16  14.52  14.74  12.79  12.150\n",
       "10  28.86  13.40  16.23  13.78  14.46  13.44  11.830\n",
       "11  29.82  13.24  16.92  13.85  15.31  14.29  12.020\n",
       "12  33.94  13.05  17.42  13.32  16.46  13.61  13.550\n",
       "13  34.64  11.39  18.14  12.62  17.18  14.06  13.386\n",
       "14  27.86  12.47  17.02  11.85  14.70  15.04  11.580\n",
       "15  26.69  15.14  15.89  12.24  12.77  15.01  10.660\n",
       "16  23.00  15.04  14.95  13.60  12.32  15.14  10.080\n",
       "17  18.56  15.38  13.61  14.54  13.91  16.89   9.590\n",
       "18  12.73  17.74  13.94  15.65  14.40  19.61  10.580\n",
       "19  15.61  19.24  13.93  18.25  15.09  19.29  11.290\n",
       "20  14.40  26.48  14.64  18.42  16.05  19.68  12.150\n",
       "21  12.20  28.53  14.40  17.31  15.87  19.15  11.920\n",
       "22  15.95  40.29  14.22  16.42  15.55  17.49  11.370\n",
       "23  17.15  32.08  14.29  16.50  15.77  16.56  12.020"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 一组数据情况\n",
    "index = []\n",
    "for i in range(len(dataDict[dateKey])):\n",
    "    index.append(str(i) + '组')\n",
    "\n",
    "data = pd.DataFrame(np.array(priceDict[priceKey]).T, columns =index)\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0组</th>\n",
       "      <th>1组</th>\n",
       "      <th>2组</th>\n",
       "      <th>3组</th>\n",
       "      <th>4组</th>\n",
       "      <th>5组</th>\n",
       "      <th>6组</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0组</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.724134</td>\n",
       "      <td>0.826076</td>\n",
       "      <td>-0.718205</td>\n",
       "      <td>0.121327</td>\n",
       "      <td>-0.890397</td>\n",
       "      <td>0.711138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1组</th>\n",
       "      <td>-0.724134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.670308</td>\n",
       "      <td>0.706366</td>\n",
       "      <td>0.217439</td>\n",
       "      <td>0.669509</td>\n",
       "      <td>-0.409407</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2组</th>\n",
       "      <td>0.826076</td>\n",
       "      <td>-0.670308</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.712149</td>\n",
       "      <td>-0.007817</td>\n",
       "      <td>-0.916284</td>\n",
       "      <td>0.810855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3组</th>\n",
       "      <td>-0.718205</td>\n",
       "      <td>0.706366</td>\n",
       "      <td>-0.712149</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.174843</td>\n",
       "      <td>0.787826</td>\n",
       "      <td>-0.498164</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4组</th>\n",
       "      <td>0.121327</td>\n",
       "      <td>0.217439</td>\n",
       "      <td>-0.007817</td>\n",
       "      <td>0.174843</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.035153</td>\n",
       "      <td>0.365808</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5组</th>\n",
       "      <td>-0.890397</td>\n",
       "      <td>0.669509</td>\n",
       "      <td>-0.916284</td>\n",
       "      <td>0.787826</td>\n",
       "      <td>0.035153</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.802066</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6组</th>\n",
       "      <td>0.711138</td>\n",
       "      <td>-0.409407</td>\n",
       "      <td>0.810855</td>\n",
       "      <td>-0.498164</td>\n",
       "      <td>0.365808</td>\n",
       "      <td>-0.802066</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0组        1组        2组        3组        4组        5组        6组\n",
       "0组  1.000000 -0.724134  0.826076 -0.718205  0.121327 -0.890397  0.711138\n",
       "1组 -0.724134  1.000000 -0.670308  0.706366  0.217439  0.669509 -0.409407\n",
       "2组  0.826076 -0.670308  1.000000 -0.712149 -0.007817 -0.916284  0.810855\n",
       "3组 -0.718205  0.706366 -0.712149  1.000000  0.174843  0.787826 -0.498164\n",
       "4组  0.121327  0.217439 -0.007817  0.174843  1.000000  0.035153  0.365808\n",
       "5组 -0.890397  0.669509 -0.916284  0.787826  0.035153  1.000000 -0.802066\n",
       "6组  0.711138 -0.409407  0.810855 -0.498164  0.365808 -0.802066  1.000000"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 相关系数矩阵\n",
    "corr_matrix = data.corr()\n",
    "corr_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0组', '1组', '2组', '3组', '4组', '5组', '6组']"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 热力图数据准备\n",
    "# X轴和Y轴所需显示内容，就是表格中的表头,注意转接成list格式   \n",
    "axis_data = list(corr_matrix.columns)\n",
    "axis_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# value值\n",
    "# 保留小数点后两位\n",
    "temp = []\n",
    "value = []\n",
    "for i in range(len(axis_data)):\n",
    "    for j in range(len(corr_matrix.iloc[i])):\n",
    "#         print(i, j , corr_matrix.iloc[i][j])\n",
    "        temp.append(i)\n",
    "        temp.append(j)\n",
    "        temp.append(round(corr_matrix.iloc[i][j],2))\n",
    "        value.append(temp)\n",
    "        temp = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# 展示相关系数矩阵\n",
    "# value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 导入pyecharts动态图标数据分析包\n",
    "from pyecharts import options as opts\n",
    "from pyecharts.charts import HeatMap"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "<script>\n",
       "    require.config({\n",
       "        paths: {\n",
       "            'echarts':'https://assets.pyecharts.org/assets/echarts.min'\n",
       "        }\n",
       "    });\n",
       "</script>\n",
       "\n",
       "        <div id=\"faaf6313069d470cab1ebd510e97f0d7\" style=\"width:900px; height:500px;\"></div>\n",
       "\n",
       "<script>\n",
       "        require(['echarts'], function(echarts) {\n",
       "                var chart_faaf6313069d470cab1ebd510e97f0d7 = echarts.init(\n",
       "                    document.getElementById('faaf6313069d470cab1ebd510e97f0d7'), 'white', {renderer: 'canvas'});\n",
       "                var option_faaf6313069d470cab1ebd510e97f0d7 = {\n",
       "    \"animation\": true,\n",
       "    \"animationThreshold\": 2000,\n",
       "    \"animationDuration\": 1000,\n",
       "    \"animationEasing\": \"cubicOut\",\n",
       "    \"animationDelay\": 0,\n",
       "    \"animationDurationUpdate\": 300,\n",
       "    \"animationEasingUpdate\": \"cubicOut\",\n",
       "    \"animationDelayUpdate\": 0,\n",
       "    \"color\": [\n",
       "        \"#c23531\",\n",
       "        \"#2f4554\",\n",
       "        \"#61a0a8\",\n",
       "        \"#d48265\",\n",
       "        \"#749f83\",\n",
       "        \"#ca8622\",\n",
       "        \"#bda29a\",\n",
       "        \"#6e7074\",\n",
       "        \"#546570\",\n",
       "        \"#c4ccd3\",\n",
       "        \"#f05b72\",\n",
       "        \"#ef5b9c\",\n",
       "        \"#f47920\",\n",
       "        \"#905a3d\",\n",
       "        \"#fab27b\",\n",
       "        \"#2a5caa\",\n",
       "        \"#444693\",\n",
       "        \"#726930\",\n",
       "        \"#b2d235\",\n",
       "        \"#6d8346\",\n",
       "        \"#ac6767\",\n",
       "        \"#1d953f\",\n",
       "        \"#6950a1\",\n",
       "        \"#918597\"\n",
       "    ],\n",
       "    \"series\": [\n",
       "        {\n",
       "            \"type\": \"heatmap\",\n",
       "            \"name\": \"\\u76f8\\u5173\\u7cfb\\u6570\\u77e9\\u9635\",\n",
       "            \"data\": [\n",
       "                [\n",
       "                    0,\n",
       "                    0,\n",
       "                    1.0\n",
       "                ],\n",
       "                [\n",
       "                    0,\n",
       "                    1,\n",
       "                    -0.72\n",
       "                ],\n",
       "                [\n",
       "                    0,\n",
       "                    2,\n",
       "                    0.83\n",
       "                ],\n",
       "                [\n",
       "                    0,\n",
       "                    3,\n",
       "                    -0.72\n",
       "                ],\n",
       "                [\n",
       "                    0,\n",
       "                    4,\n",
       "                    0.12\n",
       "                ],\n",
       "                [\n",
       "                    0,\n",
       "                    5,\n",
       "                    -0.89\n",
       "                ],\n",
       "                [\n",
       "                    0,\n",
       "                    6,\n",
       "                    0.71\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    0,\n",
       "                    -0.72\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    1,\n",
       "                    1.0\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    2,\n",
       "                    -0.67\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    3,\n",
       "                    0.71\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    4,\n",
       "                    0.22\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    5,\n",
       "                    0.67\n",
       "                ],\n",
       "                [\n",
       "                    1,\n",
       "                    6,\n",
       "                    -0.41\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    0,\n",
       "                    0.83\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    1,\n",
       "                    -0.67\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    2,\n",
       "                    1.0\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    3,\n",
       "                    -0.71\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    4,\n",
       "                    -0.01\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    5,\n",
       "                    -0.92\n",
       "                ],\n",
       "                [\n",
       "                    2,\n",
       "                    6,\n",
       "                    0.81\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    0,\n",
       "                    -0.72\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    1,\n",
       "                    0.71\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    2,\n",
       "                    -0.71\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    3,\n",
       "                    1.0\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    4,\n",
       "                    0.17\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    5,\n",
       "                    0.79\n",
       "                ],\n",
       "                [\n",
       "                    3,\n",
       "                    6,\n",
       "                    -0.5\n",
       "                ],\n",
       "                [\n",
       "                    4,\n",
       "                    0,\n",
       "                    0.12\n",
       "                ],\n",
       "                [\n",
       "                    4,\n",
       "                    1,\n",
       "                    0.22\n",
       "                ],\n",
       "                [\n",
       "                    4,\n",
       "                    2,\n",
       "                    -0.01\n",
       "                ],\n",
       "                [\n",
       "                    4,\n",
       "                    3,\n",
       "                    0.17\n",
       "                ],\n",
       "                [\n",
       "                    4,\n",
       "                    4,\n",
       "                    1.0\n",
       "                ],\n",
       "                [\n",
       "                    4,\n",
       "                    5,\n",
       "                    0.04\n",
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       "                [\n",
       "                    4,\n",
       "                    6,\n",
       "                    0.37\n",
       "                ],\n",
       "                [\n",
       "                    5,\n",
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       "                ],\n",
       "                [\n",
       "                    5,\n",
       "                    1,\n",
       "                    0.67\n",
       "                ],\n",
       "                [\n",
       "                    5,\n",
       "                    2,\n",
       "                    -0.92\n",
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       "                [\n",
       "                    5,\n",
       "                    3,\n",
       "                    0.79\n",
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       "                [\n",
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       "                    0.04\n",
       "                ],\n",
       "                [\n",
       "                    5,\n",
       "                    5,\n",
       "                    1.0\n",
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       "                [\n",
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       "                    -0.8\n",
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       "                    6,\n",
       "                    0,\n",
       "                    0.71\n",
       "                ],\n",
       "                [\n",
       "                    6,\n",
       "                    1,\n",
       "                    -0.41\n",
       "                ],\n",
       "                [\n",
       "                    6,\n",
       "                    2,\n",
       "                    0.81\n",
       "                ],\n",
       "                [\n",
       "                    6,\n",
       "                    3,\n",
       "                    -0.5\n",
       "                ],\n",
       "                [\n",
       "                    6,\n",
       "                    4,\n",
       "                    0.37\n",
       "                ],\n",
       "                [\n",
       "                    6,\n",
       "                    5,\n",
       "                    -0.8\n",
       "                ],\n",
       "                [\n",
       "                    6,\n",
       "                    6,\n",
       "                    1.0\n",
       "                ]\n",
       "            ],\n",
       "            \"label\": {\n",
       "                \"show\": true,\n",
       "                \"position\": \"inside\",\n",
       "                \"margin\": 8\n",
       "            }\n",
       "        }\n",
       "    ],\n",
       "    \"legend\": [\n",
       "        {\n",
       "            \"data\": [\n",
       "                \"\\u76f8\\u5173\\u7cfb\\u6570\\u77e9\\u9635\"\n",
       "            ],\n",
       "            \"selected\": {\n",
       "                \"\\u76f8\\u5173\\u7cfb\\u6570\\u77e9\\u9635\": true\n",
       "            },\n",
       "            \"show\": true,\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10,\n",
       "            \"itemWidth\": 25,\n",
       "            \"itemHeight\": 14\n",
       "        }\n",
       "    ],\n",
       "    \"tooltip\": {\n",
       "        \"show\": true,\n",
       "        \"trigger\": \"item\",\n",
       "        \"triggerOn\": \"mousemove|click\",\n",
       "        \"axisPointer\": {\n",
       "            \"type\": \"line\"\n",
       "        },\n",
       "        \"showContent\": true,\n",
       "        \"alwaysShowContent\": false,\n",
       "        \"showDelay\": 0,\n",
       "        \"hideDelay\": 100,\n",
       "        \"textStyle\": {\n",
       "            \"fontSize\": 14\n",
       "        },\n",
       "        \"borderWidth\": 0,\n",
       "        \"padding\": 5\n",
       "    },\n",
       "    \"xAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"0\\u7ec4\",\n",
       "                \"1\\u7ec4\",\n",
       "                \"2\\u7ec4\",\n",
       "                \"3\\u7ec4\",\n",
       "                \"4\\u7ec4\",\n",
       "                \"5\\u7ec4\",\n",
       "                \"6\\u7ec4\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"yAxis\": [\n",
       "        {\n",
       "            \"show\": true,\n",
       "            \"scale\": false,\n",
       "            \"nameLocation\": \"end\",\n",
       "            \"nameGap\": 15,\n",
       "            \"gridIndex\": 0,\n",
       "            \"inverse\": false,\n",
       "            \"offset\": 0,\n",
       "            \"splitNumber\": 5,\n",
       "            \"minInterval\": 0,\n",
       "            \"splitLine\": {\n",
       "                \"show\": false,\n",
       "                \"lineStyle\": {\n",
       "                    \"show\": true,\n",
       "                    \"width\": 1,\n",
       "                    \"opacity\": 1,\n",
       "                    \"curveness\": 0,\n",
       "                    \"type\": \"solid\"\n",
       "                }\n",
       "            },\n",
       "            \"data\": [\n",
       "                \"0\\u7ec4\",\n",
       "                \"1\\u7ec4\",\n",
       "                \"2\\u7ec4\",\n",
       "                \"3\\u7ec4\",\n",
       "                \"4\\u7ec4\",\n",
       "                \"5\\u7ec4\",\n",
       "                \"6\\u7ec4\"\n",
       "            ]\n",
       "        }\n",
       "    ],\n",
       "    \"title\": [\n",
       "        {\n",
       "            \"text\": \"\\u76f8\\u5173\\u7cfb\\u6570\\u77e9\\u9635\\u4e0e\\u70ed\\u529b\\u56fe\",\n",
       "            \"padding\": 5,\n",
       "            \"itemGap\": 10\n",
       "        }\n",
       "    ],\n",
       "    \"visualMap\": {\n",
       "        \"show\": true,\n",
       "        \"type\": \"continuous\",\n",
       "        \"min\": 0,\n",
       "        \"max\": 1,\n",
       "        \"inRange\": {\n",
       "            \"color\": [\n",
       "                \"#50a3ba\",\n",
       "                \"#eac763\",\n",
       "                \"#d94e5d\"\n",
       "            ]\n",
       "        },\n",
       "        \"calculable\": true,\n",
       "        \"inverse\": false,\n",
       "        \"splitNumber\": 5,\n",
       "        \"orient\": \"vertical\",\n",
       "        \"bottom\": 50,\n",
       "        \"right\": 25,\n",
       "        \"showLabel\": true,\n",
       "        \"itemWidth\": 20,\n",
       "        \"itemHeight\": 140,\n",
       "        \"borderWidth\": 1\n",
       "    }\n",
       "};\n",
       "                chart_faaf6313069d470cab1ebd510e97f0d7.setOption(option_faaf6313069d470cab1ebd510e97f0d7);\n",
       "        });\n",
       "    </script>\n"
      ],
      "text/plain": [
       "<pyecharts.render.display.HTML at 0x1783f9b8668>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Jupyter中显示\n",
    "c = (\n",
    "    HeatMap()\n",
    "    .add_xaxis(axis_data)   # x轴的表示内容\n",
    "    .add_yaxis(\n",
    "        \"相关系数矩阵\",\n",
    "        axis_data,      # y轴的表示内容\n",
    "        value,       # 数值矩阵\n",
    "        label_opts=opts.LabelOpts(is_show=True, position=\"inside\"),\n",
    "    )\n",
    "    .set_global_opts(\n",
    "        title_opts=opts.TitleOpts(title=\"相关系数矩阵与热力图\"),\n",
    "        visualmap_opts=opts.VisualMapOpts(\n",
    "            min_=0, max_=1, is_calculable=True, border_width=1, orient='vertical', pos_right=25, pos_bottom=50\n",
    "        ),\n",
    "    )\n",
    ")\n",
    "c.render_notebook()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 经过数据分析：0-2/6、1-3/5；0/1/2/3/5/6组数据具有参考性，剔除第四组数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[201912, 202001, 202002, 202003, 202004, 202005, 202006, 202007, 202008, 202009, 202010, 202011, 202012, 202101, 202102, 202103, 202104, 202105, 202106, 202107, 202108, 202109, 202110, 202111], [201712, 201801, 201802, 201803, 201804, 201805, 201806, 201807, 201808, 201809, 201810, 201811, 201812, 201901, 201902, 201903, 201904, 201905, 201906, 201907, 201908, 201909, 201910, 201911], [201512, 201601, 201602, 201603, 201604, 201605, 201606, 201607, 201608, 201609, 201610, 201611, 201612, 201701, 201702, 201703, 201704, 201705, 201706, 201707, 201708, 201709, 201710, 201711], [201312, 201401, 201402, 201403, 201404, 201405, 201406, 201407, 201408, 201409, 201410, 201411, 201412, 201501, 201502, 201503, 201504, 201505, 201506, 201507, 201508, 201509, 201510, 201511], [200912, 201001, 201002, 201003, 201004, 201005, 201006, 201007, 201008, 201009, 201010, 201011, 201012, 201101, 201102, 201103, 201104, 201105, 201106, 201107, 201108, 201109, 201110, 201111], [200712, 200801, 200802, 200803, 200804, 200805, 200806, 200807, 200808, 200809, 200810, 200811, 200812, 200901, 200902, 200903, 200904, 200905, 200906, 200907, 200908, 200909, 200910, 200911]]\n"
     ]
    }
   ],
   "source": [
    "filePrice = []\n",
    "fileData = []\n",
    "\n",
    "for i in range(len(dataDict['12月份日期开始+24个月'])):\n",
    "    if i != 4:\n",
    "        fileData.append(dataDict['12月份日期开始+24个月'][i])\n",
    "        \n",
    "for i in range(len(priceDict['12月份价格开始+24个月'])):\n",
    "    if i != 4:\n",
    "        filePrice.append(priceDict['12月份价格开始+24个月'][i])\n",
    "        \n",
    "print(fileData)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[33.55, 36.64, 37.2, 35.03, 33.09, 29.23, 34.53, 37.36, 37.43, 33.74, 28.86, 29.82, 33.94, 34.64, 27.86, 26.69, 23.0, 18.56, 12.73, 15.61, 14.4, 12.2, 15.95, 17.15], [14.92, 14.91, 13.43, 10.49, 10.1, 10.63, 11.56, 13.17, 13.48, 14.01, 13.4, 13.24, 13.05, 11.39, 12.47, 15.14, 15.04, 15.38, 17.74, 19.24, 26.48, 28.53, 40.29, 32.08], [16.55, 18.1, 18.14, 19.75, 20.41, 21.12, 19.91, 18.22, 18.5, 17.16, 16.23, 16.92, 17.42, 18.14, 17.02, 15.89, 14.95, 13.61, 13.94, 13.93, 14.64, 14.4, 14.22, 14.29], [15.38, 12.35, 11.92, 10.77, 10.55, 13.37, 13.01, 13.97, 15.35, 14.52, 13.78, 13.85, 13.32, 12.62, 11.85, 12.24, 13.6, 14.54, 15.65, 18.25, 18.42, 17.31, 16.42, 16.5], [12.78, 11.64, 10.35, 9.63, 10.01, 9.7, 10.37, 12.84, 12.68, 12.79, 13.44, 14.29, 13.61, 14.06, 15.04, 15.01, 15.14, 16.89, 19.61, 19.29, 19.68, 19.15, 17.49, 16.56], [16.335, 16.819, 17.309, 17.397, 16.805, 15.84, 15.55, 14.68, 14.1, 12.15, 11.83, 12.02, 13.55, 13.386, 11.58, 10.66, 10.08, 9.59, 10.58, 11.29, 12.15, 11.92, 11.37, 12.02]]\n"
     ]
    }
   ],
   "source": [
    "print(filePrice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JReqdMb3p0rSe05FERER8xye3w9pP4L7NEBzqnWsseAFmPgQ3zYImPbxzDQeoRJ2gvMLN715ZyLJthwAwwJAOidzQrwWtEqJIqBPmtRXGTsf2g0U8Nn0dX67ZQ1L9CB6+sANDOyT6VEbxT8u25XP7lGXkFZbwyEXtuS492W//XO3I9xSpgmNlvHNjb7omqUiJiIjgroCn2kLLgXDFRO9dp/gwPNMR2gzx7nVqWFVLVGDdEXaCgqIyrn9jCcu2HcIVZHAZCAsJ4paBrejTKo4GMeE+90NkUv1IXh6RyuQxvYkIcXHL21mMnLiYTfsKnY4mfspay6T5uVz5ykKMgQ9/34cRfZr73J/9qmgaG8m7N6dTLzKE6yYsYvn2Q05HEhERcd62DCjaDynVsMHurwmP8azOt+ZjOLTNu9fyYQE5EpWTd4Qxb2ayPb+Ixy/rTMuEaL/b5Lasws07GVt55qsNFJVWMLJPc+4c3Ia6EVoSXU5NYXEZf/xoFdNX7WZw+wb863fdAmpJ/Z2HjnHNqxnkHy3lrRt70b2Zf/zdFhER8Yov/wRLXof7N0OYl+93LtgBz3WFXrfAeY9791o1pNZP55u3cT+3Tc4i2BXEKyNS/X7FuwNHSnhq5gbeXbKN+pGh3H9eO36XmkSQlkSXX7Fu92Fum7yUbQeLuG9YO27u3zIg/8zsOnSMa17L4OCRUt68sRc9VKRERKQ2shae6wIJ7WH4+zVzzf/cDNnT4e41EOH/U+tr9XS+txduYdQbi2lUN4JPxvbz+wIFEBcdxhOXd+az28+iRXwUD3y0ikvGzydr60Gno4mPej9zO5eOn8/RknKmjOnNrQNbBWSBAmhcL4J3b06nfnQoIycsJmtrvtORREREat6eVZ6pde29PJXvRH1uh9IjkDWp5q7pQwKiRJVVuHnk49U88skaBrVL4KPb+pJUP9LpWNWqU5O6fHBrH567uht5hSX89qWF3P3ecvYeLnY6mviIY6UV3PfBCu7/cCWpybFMv6M/vVvGOR3L6xrVjeC9m/sQHx3KyAmL9AaDiIjUPtnTwARB2/Nr7pqNukCLgbDoZSgvrbnr+gi/L1GeBSQW83bGVm4Z2JJXRqQR7WfLNp8qYwyXdGvCN/cOZOygVkxfuZtBT83mpdmbKSmvcDqeOCgn7wiXvTifD7J2MO6c1rx9Y28S6oQ5HavGNKwbzrs396FBTDgjJywmc4uKlIiI1CLrpkFSOkQn1Ox1+94BhbthzX9q9ro+wK9L1Oa8I1z64nyW5Obz5BVdePD89rgCdNrSiaLCgrlvWApf3TOAfq3j+ceX2Qx75ju+WbcXb9/jJr5n+srd/OaF+ew9XMykG3py79B2teLvwU95ilQ6iTHhjJy4mCUqUiIiUhsczIF9a2p2Kt9xrc+FBh1gwfOe+7JqEb8tUXM35nHp+PkcPlbGlJt687u0JKcj1bjkuCheG5nGW6N74Qoy3PhmJjdMWsLmvCNOR5MaUFru5v8+XcPYKUtpkxjN9Dv6c3a7Bk7HclRiTDhTb06nYd1wRk1czOJcFSkREQlw2dM9H729tPnPMQb6jIW9qyFnVs1f30F+V6Kstby5YAvXv7GEJvUi+OT2fqQFwAISZ2JA2wS+vGsAD1/Ynqwt+Qx75jse/3wdhcVlTkcTL9l56BhXvrKQSQu2MLpfC967uQ+N60U4HcsnJMaE8+5N6TSqG871byxmUc4BpyOJiIh4z7pp0LAzxCY7c/3Ov4PoRM9oVC3iVyWqrMLNwx+v5i+frmFQuwZ8+Pu+NI0NrAUkTleIK4gx/Vvy7R/O5rc9mvLa3BwGPTWHD7N24HbXruHVQDdr/T4u/PdcNu87wkvDe/DnizsQGuxXf5W9rkHliFTjehFc/8YSMlSkREQkEB3ZB9sXOTMKdVxwGPS+BTZ/C3tWO5ejhp3ST17GmPrGmCHGmHhvB/olh4pKGTVxMZMXbePWga14dURqwC4gcSYS6oTxjyu68PFt/UiqH8EfPljBZS8tYPn2Q05HkzNUXuHmyRnZ3PDGEhrVjeCzcWdxfudGTsfyWQ3qhDP1pnSaxkZwwxtLWLB5v9ORREREqlf2dMA6W6IAUm+AkChYON7ZHDXopCXKGBMLTAN6AbOMMQnGmG3GmNmV/3X2dshN+45w6fj5ZG7J51+/68ofz08J2H1vqkvXpHp8dGtfnr6yK7sOHePS8fO574MV7CvUkuj+aF9hMddNWMT4WZu5Ki2J/97Wl+bxUU7H8nkJdcKYUlmkRk9awoJNKlIiIhJAsqdDbHNI7Ohsjsj60P06WPUBHN7lbJYaYk62mpsxZiBQYq3NMMY8BeQB9a21D5zKBdLS0mxmZuZpB/xuQx5jpywlLDiIV0akkppcu+9/Oh1HSsp5/tuNTJyXS1iwi9/2aEJcdBj9WseTmhzrdDw5iYycA4ybuozC4jIevbQzV6Q2dTqS39l/pIRrX8tg28EiJozqSb/Wjg2qi4iIVI/iw/BkK+h1Mwx7zOk0cDAXnu/hWfZ8yF+dTlNlxpgsa23aqR5/0pEoa+2cygI1AM9o1DHgImPMYmPMBGOMV+bUWWt5Y34u17+xmCb1Ivh4bD8VqNMUHRbMg+e3Z8ZdA2jXsA5vLtzK019t4OpXF2oZaB/mdltenL2Ja1/LoE5YMB+P7acCdZrio8OYelM6zeOiGD1pCfM2akRKRET83MaZUFEK7S92OolH/RbQ/jeQ+QaUFDqdxutO9Z4oA1wF5APLgMHW2l5ACHDBzxx/szEm0xiTmZeXV+VQZRVu/vTf1fz1s7UMbp/IR1pAolq0TIjmnJQGmMqZkGUVlt+/k8WMNXu0v5SPOVRUyk1vZfLPL9dzQedGfDruLFIaxjgdy6/FRYcxeUxvWsRHceObS5i7serfm0RERHxG9jSIagBNezqd5Ad9x0FJASx7x+kkXndKJcp6jAVWAo2ttbsrn8oE2vzM8a9aa9OstWkJCVXbOTn/aCkjJixi6uJt3HZ2K16+LpUoLSBRbdJbxhEWHITLQIjLEOoK4pa3s7j0xQXM27hfZcoHrNh+iAv/PY/vNubx/y7pyPPXdNciKtUkLtpzj5SnSGXy3QYVKTl9WVvzGT9rE1lb852OIiK1TVkxbPwK2p0PQS6n0/ygaRo06wsLX4SKcqfTeNVJfzIzxjwA7LbWvgXUA142xmQDq4FLgcerK8ymfYXc+GYmuwuKeeaqrlzWXVOXqltqciyTx6STkXOA9JZxdG1al/8s3cmzX2/gugmL6NMyjj8Ma6d7pRyQteUgL87ZzOz1+2gYE8EHt/alW1I9p2MFnPpRoUy9KZ1rX1/EmLcyeW1kGgPbVu3NHqm9rLXsKijmsxW7eGrGetzWEhocxOQx6fq+KSI1J3cOlB7xnal8J+p7O7x7Laz7BDr91uk0XnMqC0vEAu8DYXiK00vAZMAAn1prH/q115/qwhKz1+9j3JRlhIUE8cqINP1jVMNKyiuYumgbL8zaxP4jpZyb0oB7h7ajQ2NNIasJi3IOcM1rGbgtBBl44/qeDGzXwOlYAS3/aCnDX1/EprwjvDIilUH6/ZafKKtwsznvCGt3HWbtrsOs2XWYtbsPU3DsxxuZuwzcM7QdYwe1diipiNQ6n46D1f+F+zd79mnyJW43vJAG4TFw0yy+v4/Ex1V1YYmTjkRZa/OBIT95uEtVg/3K+Xlj/hYenb6Wdg1jeH1UGk3qRVTX6eUUhQW7uL5fC67smcQb87fwypzNXPDvuVzctTF3D25Dy4RopyMGtH99tYHjeyIbYPWuwypRXhYbFcqUm3oz/PVF3PJWlqdIpej3vLY6UlLOut2Hvy9Ma3cfZv3eQkrL3QCEBQeR0iiGCzo3okPjGIKDDI98vJpyt8XlCiK9ZZzDvwIRqTXcFZD9ObQd6nsFCiAoyDMaNe1u2LoAmvdzOpFXOHqjRWm5m798upqpi7czrGMiT1/ZTfc/OSwyNJixg1pzXe9kXp27mYnztvD5qt1c0aMpdwxuo4LrBYtyDrA49yCuIAPWEhKsH8hqSr3IUCaP6c11ExZxy9tZvDyiB+ekJDodS7zIWsu+wpLKkaUC1lYWpy0Hir4/pn5UKB0bx3BD3+Z0aBxDh0YxtIiPItj149uIk+tHctNbmTStH0mPZpp6KyI1ZPsiKNrv/Aa7v6brNfDto7Dg+YAtUSedznemfmk6X/7RUm59J4tFuQe5fVBr7hnSVhvo+qC8whJenL2JyRnbABie3ozbzm5NQh0ffOfDDx0uLuP8Z+cS4jI8dllnlm8/RHrLOE1nrWEFRWVcN2ER2XsO8/J1qZzbXkUqEFS4Lbn7j3w/De/4KNOBo6XfH5McF0mHRp6i1LFJDB0a1SUxJgxzitNP3l28jT/+ZxUvX5fKeZ0aeuuXIiLygy//BEteg/s2e6bM+apZT8Ccv8PYJZDQ1uk0J1XV6XyOlKiNez0LSOw5XMw/f9uFS7s38WoGOXM7Dx3j319v5MOlOwh1BTH6rObc3L8VdSNDnI7m1+55bzmfrNjFh7f2oXszFScnFRwrY8SERazbfZiXhqcyuIOKlD85VlpB9p7DPypM2XsOU1zmmY4X6gqibcPo7wtTh8Z1ad+oDnXCz+x7WHmFmwv+PZfyCsuMuwcQ4jqlRW9FRE6PtfBcF0hIgeEfOJ3m1x3Jg2c7QZer4Df/djrNSfl8iZq1fh93TFlGWIiL10am6gdHP5OTd4Rnvt7IZyt2ERMezC0DW3FDv+ZEhmoaZlVNW7mL26cs467BbbhrsO+/Q1MbFBwrY+SERazdfZjx1/ZgaEeNLPii7zbk8dmKXYSHuCg4VsaaXQXk7j/6/X2FMeHBldPw6tKxcQwdGsfQKiGa0GDvFJxvs/cyelImf7ukIyP6NPfKNUREANizCl4+Cy7+N6SOcjrNyX12FyyfAnevgWjfXgnXZ0uUtZYJ83J5/PN1pFQuINFY99f4rTW7Cnh65ga+yd5HfHQoYwe15trezQgL9qG9CnzYnoJihj37HS3io/jw1j7/c6+FOOdwcRkjJixm7a4C7h7SFmvRFEsf8vGyndz13vLvv46PDqV7s9jK0aUYOjaOoUm9iFOejlcdrLVc81oGG/ceYc79g7Svm4h4z6wn4Lt/wr0bfL6UALB/I7zQEwbeD4P+5HSaX+WTJWpBxmIe+Xg172Vu57yODXn6qq4auQgQWVvzeXJGNhk5B2lSL4I7z23D5T2aqBT8CrfbMnLiYpZuy2f6Hf1pER/ldCT5icPFZfz2xQVs3HcEA4SFaB8gX7Dr0DGGPfMdhSWeDRx9aWnxFdsPccn4+dxxTmvuGdrO6TgiEqhe6gdhMTD6C6eTnLqp18C2DM9oVGik02l+UVVLlNd/0t17uJhLxs/jvcztjDunNS8O76ECFUBSk2OZelM679zYm/joUO7/aCVDn/mOaSt34XZ7t6D7q0kLtjBv034euaiDCpSPigkP4YLOnql8Fs9Kohk5B5wNVcsdKipl1MTFVLg9m9u6DD61kmXXpHpc3LUxr83NZe/hYqfjiEggOpgLe1dDyoVOJ6mavuPg2EFYMcXpJNXK6yVqX2EJ63YXctfgNtw7tJ1W4AtAxhjOahPPx2P78cqIVIJdhtunLOPC5+fxbfZevD3a6U/W7ynk719mM7h9Ilf3THI6jvyKAW0bEFZ5D4210LlJXYcT1V7FZRWMeTOTrQeKeP36NKbelM49Q9v53OjgfUPbUe528+zXG5yOIiKBKHua52N7H17a/Oc06wNNUmHheM8eVwGiRuZcBRm0YlEtYIxhWMeGfHHnAJ69qhtHS8oZPSmTK15eqHfxgZLyCu56bzkx4cH8/beda/SeDam61ORYptyUzvDezQgy8E7GVr0h4IDyCjfjpi4ja1s+z1zVjb6t4klNjmXsoNY+VaAAmsVFMrJPc95bsp0NewudjiMigWbdNEjsDLHNnU5SNcZ4RqMO5sB6P5qGeBI10mxCfWjKhXifK8hwafcmfHPvQB67rBM7849x9asZjJiwiBXbDzkdzzFPf7WBdbsP888ruhAfrX22/EFqciyPXdaZP13YgZlr9zJhXq7TkWoVay2PfLKar9bu5f8u7siFXRo5Hemkbh/UmqgA72ARAAAgAElEQVSwYP7xRbbTUUQkkBzZ59lk199GoY5LuRjqNfNsvhsgvF6iEmPCfW7KhdSMEFcQw3snM/u+s3n4wvas3lnAJePnc8vbmWzYW0jW1nzGz9pE1tZ8p6N6XUbOAV79LodrezfjnBTtP+RvRvdrznkdG/L3L7LJ2nrQ6Ti1xrNfb2Tq4u2MHdSKUX2bOx3nlMRGeVYr/SZ7Hws3awReRKrJ+s8BCyl+WqJcwZA+FrZnwPbFTqepFo5stiu1U2FxGRPnbeG1uTkcKSnHZQwWz03igVy0DxeXcf6zcwkNDmL6HWdpYRU/dbi4jIufn0dJmZvpd5xFnEYTveqdjK08/PFqrkxryj9+28Wvpr8Wl1VwzlOzia8Txse39dO9wCJy5t65Ag5shDuWe6bH+aOSI/BMB2gxEK562+k0/8PnVucTOa5OeAh3Dm7D3PsH0atFLBXW4raBv/LZXz5Zw57DxTxzVTcVKD8WEx7Ci8N7cLColLveW06FVp/0mi9X7+HPn6zmnJQGPH6Z/90/GB7i4t6h7Vi5o4Dpq3Y7HUdE/F3xYcid4xmF8rPvhz8SFg1pN3oWyDiY43SaM6YSJTUuNiqUB85rT+gJK5+1a1jH4VTe8dmKXfx32U7uOKcN3ZLqOR1HzlDHxnX56286MnfjfsbP2uR0nIC0OPcgd7y7jK5J9Rh/bQ+/3XPu0u5NaN8ohn/OyKakPHBWoxIRB2ycCRWl/juV70S9bwHjgoyXnE5yxvzzXyfxe8f3l7q2VzNCXEE8+eV6Dh4tdTpWtdpdcIyH/ruK7s3qMXZQK6fjSDW5umcSl3VvwjNfb2D+pv1Oxwko6/cUMubNJTSNjWDiqJ5EhLqcjnTaXEGGP12QwvaDx3gnY5vTcUTEn2VPh6gESOrldJIzV6chdLkSlr0DRf59j7FKlDgmNTmWxy/vzKQbepJ74CgjJy6i4FiZ07Gqhdtt+cMHKyh3W569qpvfvpsu/8sYw2OXdaJ1QjR3vrtMG6tWk52HjjFq4mIiQl28NboXsVGhTkc6Y/3bJNC/TTzPf7sxYL63iUgNKy+BjV9BuwsgyH/fWPqRPrdDWRFkTnA6yRnRT3biuL6t43nlulTW7ynk+jcWc6Sk3OlIZ2zi/FzmbzrAny/qQHJclNNxpJpFhgbz0nU9KCqtYNyUZZRXuJ2O5Nfyj5YycsIijpaW8+boXjSNjXQ6UrX54/kpFBwr46XZm52OIiL+KGcOlBZC+4udTlJ9EjtA68Gw6FUo8983IlWixCcMSmnA89f0YOWOAsa8uYRjpf57D8H6PYX8c8Z6hnRI5KqeSU7HES9p3aAOT1zemcVbDvLkzPVOx/Fbx0oruPHNJWzPP8ZrI9NIaRjjdKRq1bFxXS7r3oSJ83PZeeiY03FExN9kfwahdaDFAKeTVK++4+DoPlj1gdNJTptKlPiM8zo15Okru7Io9yA3v53plzdjl5RXcOe7y4gJD+Hvl/vfqmJSNZd0a8K1vZvxypwcvl671+k4fqe8ws3tU5aybPshnruqW8Buyn7v0HYA/EtlW0Sqwl0B2Z9DmyEQHGDbarQYCImdPZvvuv1zNodKlPiUS7o14R+Xd2Huxv2MnbyMMj+bJvWvmRvI3lPIP6/orH2Eaok/X9SBTk1iuPeDFWw/WOR0HL9hreWh/67mm+x9/L9LOnF+50ZOR/KaJvUiuKFfc/67bCdrdhU4HUdE/MX2xVC0H9oHwKp8P2WMZzRq/3rY9LXTaU6LSpT4nCt7JvH/LunI1+v2+tV+PAs3H+C1uTlcl96Mc1ISnY4jNSQ8xMWL16bitpaxU5b65QiqE57+agPvZW7njnNaMyI92ek4Xnfb2a2pGxHC37/IdjqKiPiL7GngCoXWQ5xO4h2dLofIePj8Pk9h9DMqUeKTRvZpzp8uSGH6yt3c/+FK3D5epAqOlXHv+8tpERfFQxd0cDqO1LBmcZE89buurNxRwGPT1zkdx+e9vXALz3+7iat7JnH3kLZOx6kRdSNCGHdOG+Zu3M93G/KcjiMivs5aWPcZtDwbwgPrXtHv7VoGxYfg0BaYdJHfFSmVKPFZNw9oxT1D2vLR0h088slqrPXdIvXnT1azr7CEZ67q5td728jpG9axIWPOasFbC7fy2YpdTsfxWZ+v2s2fP13D4PaJPHppp1p13+B16c1Iqh/BE19k+/wbQyLisL2r4dBWSLnQ6STes2WupywCuMs9X/sRlSjxaePOac3vz27F5EXb+Nu0dT5ZpD5dsYtPlu/iznPb0DWpntNxxEEPnJ9CanIsf/xoJZvzjjgdx+dk5BzgrneX06NZLM9f073W7Z8WFuzivmEprNt9mI+X73Q6joj4snXTAOPZHypQNe/vma5oXJ6Pzfs7nahKate/YOJ3jDHcP6wd1/dtzsT5ufxr5ganI/3IrkPHePi/q+jRrB6/P7uV03HEYSGuIF64tjthIS5ue2epXy/VX93W7T7MTW9m0iwukgmj0mrtiO1FnRvRpWldnpqxnuIy/fkQkV+QPQ2apUN0A6eTeE9SLxj1KZzzkOdjUi+nE1WJSpT4PGMMf7m4A9f0SuKFWZt44duNTkcCwO223Pv+Circlmeu6lbr3lWXn9eobgTPXtWNDfsKeeST1U7H8Qk78osYNXExUWHBvDW6F/UiQ52O5JigIMOD57dnV0ExkxZscTqOiPiig7me6XwpAbgq308l9YL+9/pdgQKVKPETxhgevbQzl3VvwlMzN/D63BynIzFxfi4Lcw7wl4s7khwX5XQc8SED2iYwblBrPszawfuZ252O46iDR0sZOXExxWUVvHVjLxrXi3A6kuP6tIrj3JQGjJ+1ifyjpU7HERFfkz3d8zEQlzYPICpR4jdcQYYnr+jCBZ0b8uj0dbyTsdWxLNl7DvPPL9cztEMiv0tr6lgO8V13Dm5Lv9ZxPPLxatbtPux0HEcUlZYzetISduQf4/VRPWmbWMfpSD7jgfNTOFpSzguzNjkdRUR8TfY0z0a0sc2dTiK/QiVK/EqwK4hnr+rOuSkNePjj1XyYtaPGMxSXVXDXu8uJiQjhics716rVxeTUuYIMz17VnboRIdw2eSmFxWVOR6pRZRVuxk5eysodh3j+mu70alHf6Ug+pW1iHa5MS+KthVu0SbOI/ODIPtiWEdir8gUIlSjxO6HBQYwf3oOzWsdz/4cranw56X/NXE/2nkKe/F0X4qLDavTa4l8S6oTx/DXd2XawiD9+tMonV5f0Bmstf/rPKmatz+Nvl3ZiWMeGTkfySXcPaYsryPDPGeudjiIivmL954DVVD4/oBIlfik8xMWrI1NJS67P3e8tZ+aaPTVy3QWb9vP6vFxGpCczqF0Ar5gj1aZ3yzj+MLQd01ft5s1aspDAkzPW80HWDu48tw3Deyc7HcdnJcaEc1P/lny2Yhcrth9yOo6I+ILs6VAvGRI7OZ1ETkIlSvxWZGgwE2/oSacmdbl9yjLmbMjz6vUKisq494MVtIiP4k8XtPfqtSSw3DKgJeemNOCxz9exPMB/WJ40P5cXZ2/mml7NuGtwG6fj+LxbBrYiLiqUxz/3zX3wRKQGFR+GnNnQ/mLQrQI+TyVK/Fp0WDBv3tCL1g2iufmtTBZuPuC1az3yyWryCkt49qputXaPGzk9QUGGf13ZlQZ1whk7eSmHigJzRbZpK3fx12lrGdohkUcv7aT7BU9BdFgwdw1uw6Lcg8xav8/pOCLipE1fQUVp7VjaPACoRInfqxsZwts39qJZ/UhufHMJWVvzq/0anyzfyacrdnHX4DZ0aVqv2s8vga9eZCjjh/dgX2Ex97y/Arc7sEYdFmzezz3vrSAtOZZ/X9MdV5AK1Km6ulczWsRH8cTn2ZRXuJ2OIyJOWTcNIuP9cs+k2kglSgJCXHQYk8f0pkGdMK6fuJhVOwqq7dw7Dx3j4Y9Xk5ocy60DW1XbeaX26ZZUj4cv7MC32ft4+bvNTsepNmt2FXDzW1k0j4/k9ZE9CQ/RSG1VhLiCeOC8dmzcd8SRFUdFxAeUl8DGryDlAgjS91B/oBIlAaNBTDhTbkonJiKEERMXsX5P4Rmf0+22/KFy1OCZK7sR7NJfGTkzI/skc2GXRjw1Yz0ZOd6bflpTth8s4vo3lhATHsybo3tRNzLE6Uh+aVjHhvRoVo+nv9pAUWm503FEpKblfgelhZBysdNJ5BTpJ0IJKI3rRTD1pnTCgoMY/noGm/OOnNH5JszLZWHOAf7ym440i4usppRSmxlj+Mdvu9A8Loo7pi4jr7DE6Uin7cCREkZOXExpuZs3R/eiUd0IpyP5LWMMD13Ynn2FJUyYm+t0HBGpaes+g9A60HKg00nkFJ1SiTLG1DfGDDHGxHs7kMiZahYXyeQx6QAMf20R2w6c3kaW63Yf5skZ6xnWMZHfpTatzohSy0WHBTN+eA8KjpVx57vLqPDD+6OOlpQzetISdh06xsTr02iTWMfpSH4vNbk+53VsyMtzNrP/iP+WaxGpIneFZ3+oNkMgWPtP+ouTlihjTCwwDegFzDLGJBhjJhhjFhpjHvZ6QpHT0LpBNO+M6U1xeQXXvp7BrkPHqvT64rIK7np3OXUjQ3ji8i5aZUyqXftGMfzt0k4s2HyAZ7/e4HScKimrcDN2ylJW7SzghWt7kJpc3+lIAeP+89pRXO7m399sdDqKiNSU7YvhaB6kXOh0EqmCUxmJ6gLcY619DJgBnAO4rLV9gJbGGG0EIj4ppWEMb4/uTUFRGcNfX8S+wuJTfu2TM9azfm8hT17RhfpRoV5MKbXZlWlJ/C61Kc9/u4nZfrK8tbWWBz5ayez1eTx+WWeGdEh0OlJAaZkQzbW9mjFl0TZyznA6soj4iexp4AqFNkOdTiJVcNISZa2dY63NMMYMwDMaNQx4v/LpmcBZXswnckY6N63LpNE92Xu4mOteX8TBoyffn2f+pv1MmJfLyD7JnN2uQQ2klNrs/13SiZSGdbj7veVVHjGtaVlb87nq1Qz+s3Qn9wxpy9W9mjkdKSDdcW4bwoKD+OeX652OIiLeZq3nfqgWAyE8xuk0UgWnek+UAa4C8gEL7Kx86iDwP29DGmNuNsZkGmMy8/LyqiuryGlJTa7P66PS2HqgiOteX0RBUdkvHltQVMa976+gVUIUD57fvgZTSm0VEepi/PAelJa7uX3KUsp8cJ8gt9vyQeZ2rnxlIYtzD+IKMvRrFed0rICVUCeMWwa24ss1e8jaetDpOCLiTXvXwKGt0F4b7PqbUypR1mMssBLoCxxfgin6585hrX3VWptmrU1LSEiotrAip6tvq3heGZHKxn2FjHpjMUdK/ncJYWstD328iv1HSnju6u5EhGqfBqkZrRKi+ccVXVi67RB//yLb6ThYa9m07whvL9zC79/JosejX3Hfhyt/WADDWjJy9cO9N43p34IGdcJ4/PNsrPW/hUdE5BRlTwMMtLvA6SRSRaeysMQDxpiRlV/WA/7OD1P4ugJbvBNNpHqd3a4BL1zbg1U7Cxg9aQnHSit+9Pwny3cxbeVu7h7Slk5N6jqUUmqri7o0ZlSfZCbMy+XL1btr/Po78ot4P3M7d727jN6Pf8Pgp+fwyCdrWLmjgCHtE7nz3NaEBQfhMhASHER6S41EeVNkaDD3DGlL1tZ8ZqzZ63QcEfGWddOgWTpE6/YBf2NO9g5X5ep87wNhwGrgQeA74BvgfCDdWlvwS69PS0uzmZmZ1RZY5Ex9tmIXd767jH6t43ltZBrhIS525Bdx/nNzaZdYh/du6YMrSKvxSc0rKa/gypcXkpN3lGl3nEVyXJTXrpVXWMKCzftZuPkACzYfYNtBz1YA8dGh9GkVT79WcfRtFU9S/YjvV6fM2ppPRs4B0lvGkZoc67Vs4lFe4eb85+ZS7rbMvHsAIdrsWySw5G+B57rC0Eeh7zin09R6xpgsa23aKR9/OtMEKovVEOA7a+2eXztWJUp80QeZ27nvw5UMbu8ZnRo1cTFrdh3mizv7k1Rfm+qKc7YfLOKi5+fRNDaCj37fl/CQ6plWWlBURkbugcrStJ8Nez0rv8WEB5PeMo6+reLo2zqeNg2itaS/D/lm3V5ufDOTv13SkRF9mjsdR0Sq04IXYOZDcMdyqN/C6TS1XlVLVPDpXMRam88PK/SJ+J3fpSVRXO7mkY9Xc/aTs9hzuISxg1qpQInjkupH8vSVXbnxzUz++tlanri882mdp6i0nCVb8r8fbVq9swC3hYgQFz1b1OfyHk3p2yqOjo3rauTVh52T0oDeLerz7NcbuaxHU6LDTuufbRHxRdnTIbGTCpSf0ndjqbVGpCezeV8hkxZsBWDCvFzOSUnUNCVx3LntE7l1YCtenrOZXi1iuax705O+pqS8guXbDrGgcqRp+fZDlFVYQlyG7s1iuePcNvRtFU+3pHqEBmtamL8wxvDgBe25dPx8Xp2zmXuGtnM6kohUhyN5sG0hDHzA6SRymlSipFZLqBNOkAG3hbJyNxk5B1SixCf8YWhblm7N50//WU2nxnVpk1jnR89XuC2rdxYwv3KkacmWgxSXuQky0LlJXcb0b0nfVnGkJdfXSpN+rltSPS7q0ojX5uYyPD2ZxJhwpyOJyJla/zlgtbS5H1OJklotvWUcocFBlJW7teKY+JRgVxDPX9udC/89l+snLeGKHk1JjovkUFEZCzYfYFHuAQqLPUv1pzSswzW9mtG3VTy9WtSnbkSIw+mlut0/LIUZa/bwzFcb+PtvuzgdR0TOVPY0qNfMM51P/JJKlNRqqcmxTB6TrhXHxCclxoRz+6DW/N9na3num43fP948LpKLujSmb6s40lvGkVAnzMGUUhOaxUUyIr05kxbkMvqsFrT9ycikiPiR4sOQMxt63gRayMdvqURJrZeaHKvyJD7raGkFBrCAAW4e2JIHz2/vcCpxwrhzWvNB1nb+8UU2E67v6XQcETldm76GilJN5fNzurtYRMSHpbeMIyzEs8ltWEgQQzs0dDqSOCQ2KpTbzm7NN9n7WLj5gNNxROR0LX0TQiLxvDUm/kolSkTEhx2fcnrP0HZMHpOuUdNa7oZ+zWlUN5wnvliH2131fR5FxGGZEz1T+cqK4O3LYPtipxPJaVKJEhHxcanJsYwd1FoFSggPcXHv0Has3FHA9FW7nY4jIqdq/yZ4dzhMu/uHxypKYctc5zLJGVGJEhER8SOXdW9C+0Yx/HNGNiXlFU7HEZFfc3Q/fH4fvNjbMwLV43oIDgfjAlcoNO/vdEI5TVpYQkRExI+4ggwPnp/CyImLeSdjGzee1cLpSCLyU2XHIONFmPcslB6F1FFw9oMQ3QC6D/eMQDXvD0m9nE4qp0klSkRExM8MaJtA/zbxPP/tRq5Ibaq9wUR8hdsNK9+Dbx+Fwzug7fkw5K+Q0O6HY5J6qTwFAE3nExER8UMPnJdCwbEy/vLJGsbP2kTW1nynI4nUbjmz4dUB8PGtEJ0A10+Ha9/9cYGSgKGRKBERET/UqUldBrSJ5+PlOzFAaHAQU27SCo4iNW7vWvjqz7DpK6jbDH47ATpeDkEaqwhk+r8rIiLip9o3igE8mzGXlLu5Y+pSXvsuh20HipwNJlIbFO6BT8fBy/08S5UP+RvcvgQ6X6ECVQtoJEpERMRPDenQkEkLtlBa7ibIGIJdQTz2+Toe+3wdKQ3rMLRjQ4Z1TKRDoxiM0caeItWi5AgseB4W/BsqyqD3rTDgPois73QyqUHGWu9u1peWlmYzMzO9eg0REZHaKmtrPhk5B0hvGUdqcizbDhQxc+0eZq7Zy5KtB7EWmsZGMKxjQ4Z2SCSteX1cQSpUIlVWUQ7L3obZT8CRvdDxMjj3z1C/pdPJpBoYY7KstWmnfLxKlIiISGDaf6SEb9btZcaavczbuJ/SCjdxUaEMbp/I0I6J9GsdT3iIy+mYIr7NWtg403PfU142JKXD0EchqafTyaQaqUSJiIjI/zhSUs6c9XnMWLOHWdn7KCwpJyrUxdntGjC0YyKDUhoQE66l0kV+ZNdymPmwZ1+n+q1g8P9B+4tB02MDTlVLlO6JEhERqQWiw4K5sEsjLuzSiNJyNwtzDjBjzR6+WruX6at2E+Iy9GkVz9AOiQztkEiDmHCnI4s459B2+PZvnj2fIurD+U9C2g3g0hsN4qGRKBERkVrM7bYs257PzDV7mbFmD1sOFGEMdE+qV7kwRUNaxEc5HVOkZhw7BPOehoyXPaNN6b+Hs+6G8LpOJxMv03Q+EREROS3WWjbsPcLMNXuYsXYPq3ceBqBtYnTlwhQN6dREK/1JACovhcyJMOcfcCwfulwF5zwM9ZKcTiY1RCVKREREqsXOQ8c8hWrNHhbnHsRtoUm9CIZ08CxM0at5fYJd2g9H/Ji1sO5T+Pr/4GAOtBjgWTSiUVenk0kNU4kSERGRanfwaOn3K/3N3ZhHSbmb2MgQzm2fSKuEKErL3ZzVJoHU5Fino4qcmu2LPYtGbF8ECe1hyP+DNkO0aEQtpRIlIiIiXlVUWs53G/KYUXkfVVFpBQChriCm3pyuIiW+7cBm+OavsPYTiE6EQQ9Bt+Hg0nprtZlW5xMRERGvigwN5rxOjTivUyOe/3Yjz3y1AbeF0go37y3ZphIlvmnj1zDnn7AzE4LD4ewHoc/tEBbtdDLxQ5rILCIiIqetb6t4QoODcBkwwH+X7WTexv1OxxL5sc2zYfIVsGOR5+srJsDZf1SBktOmEiUiIiKnLTU5lslj0rlnaDsmXp9Gq4RobnxzCd9tyHM6msgPFjwHnHALy761jkWRwKASJSIiImckNTmWsYNaMyglkSk3pdMiPooxb2UyR0VKfEFxgWcRCRMExgWuUGje3+lU4udUokRERKTa1I8KZepN6bROiOamtzKZvX6f05Gktst4CUqPwG+eh3MeglGfQlIvp1OJn1OJEhERkWoVGxXK5DG9adMgmpvfzmKWipQ4peggLBwPKRdB9+ug/70qUFItVKJERESk2p1YpG55K4tZ2SpS4oCFL0BJIQz6k9NJJMCoRImIiIhX1Iv0FKm2DaO55e0svs3e63QkqU2O7oeMl6HjZZDY0ek0EmBUokRERMRr6kWGMvnGdNo1rMMtb2fx9VoVKakh856B8mOe/aBEqplKlIiIiHhV3cgQ3hnTm/aNYvj95Cy+UpESbyvcA0tehy5XQUJbp9NIAFKJEhEREa+rGxHC2zf2pkOjGG6bnMXMNXucjiSBbO7TUFEGA+93OokEKJUoERERqRF1I0J4e0xvOjSuy22TlzJDRUq84dB2yHoDug+H+i2dTiMB6qQlyhhT1xjzhTFmpjHmv8aYUGPMNmPM7Mr/OtdEUBEREfF/MeEhvH1jLzo1qcvYyUv5crWKlFSzuU95Pg7QKJR4z6mMRA0HnrbWDgX2AH8Eplprz678b5VXE4qIiEhAOV6kujSty+1TlvLFqt1OR5JAcTAXlr0DPUZBvSSn00gAO2mJsta+aK39qvLLBKAcuMgYs9gYM8EYE+zVhCIiIhJw6oSH8OboXnRNqsftU5fxuYqUVIfvnoSgYM+muiJedMr3RBlj+gCxwFfAYGttLyAEuOBnjr3ZGJNpjMnMy8urtrAiIiISOI4Xqe5J9Rg3dRnTV6pIyRnYvxFWTIWeYyCmkdNpJMCdUokyxtQHngdGAyuttce/y2UCbX56vLX2VWttmrU2LSEhodrCioiISGCJDgtm0uhe9GhWjzveXca0lbucjiT+avbfITgC+t3ldBKpBU5lYYlQ4APgQWvtVuBtY0xXY4wLuBRY4eWMIiIiEsCiw4J544ZepDaL5c53l/PZChUpqaK9a2H1R9D7ZojWG/jifacyEnUj0AN4yBgzG1gDvA0sBxZaa7/2XjwRERGpDTxFqiepybHc+e4yPlm+0+lI4k9mPw6h0dD3DqeTSC1x0kUhrLUvAS/95OG/eieOiIiI1FZRYcG8cX1PRk9awt3vLQfgkm5NHE4lPm/3Clj3GQz8I0TWdzqN1BLabFdERER8RlTliFSvFvW5+73l/HfZDqcjia+b9TiE14M+tzmdRGoRlSgRERHxKZGhwUy8vie9W8Rx7/sr+M9SFSn5BTsyYcOX0HcchNd1Oo3UIipRIiIi4nOOF6n0lnHc+8EKPspSkZKf8e2jEBkHvW91OonUMipRIiIi4pMiQl1MGNWTfq3i+cOHK/hQRUpOtHUB5MyCs+6GsGin00gtoxIlIiIiPisi1MXro9I4q3U89324gvcztzsdSXyBtfDtYxCdCGk3Op1GaiGVKBEREfFp4SEuXhvpKVIPfLSS95eoSNV6uXNg6zzofy+ERjqdRmohlSgRERHxeceLVP82Cdz/0UreW7LN6UjiFGs990LFNIXU651OI7WUSpSIiIj4hf/P3n3Ht1nd/f9/XZqWLe+9HSdecZZjZ0ISG5JAIOy9CrRAB/3R3u1daMvd+9sWuls67lJKKbNA2TMkkITECVkkDnGWtx1vy3vJkqxxXb8/5Ch2Bs2wLdk+zzz0kHRpXEeOLV1vnXM+x0+r5h935bIiPZJH3j7Mv/eKIDUlVW6Cxn2w/L9Bo/d2a4QpSoQoQRAEQRAmDD+tmqfvyiU/I5IfvXOYVz8XQWpKURTY+jiEJEPOnd5ujTCFiRAlCIIgCMKEcjxIFWRE8uN3D/PK53XebpIwXsrWQctBWPEIqLXebo0whYkQJQiCIAjChKPXqPn7XblckhnFo+8e4V97RJCa9GQZtv4SwmfAnFu83RphitN4uwGCIAiCIAjnQ69R89Sd83nwlS/4yXtHqOscINRfx+LUcHKTQ73dPGG0lbwLbSVww7OgFoewgneJ30BBEARBECYsvUbNk3fM545nPuefnx1DAvRaFa/ct1gEqcnE5YStv4LILMi+3tutEQQxnE8QBEEQhIlNr1GzPD0SAAWwO2X21HR6t1HC6Dr8JhGRZssAACAASURBVHRWQsGPQCUOXwXvE7+FgiAIgiBMeBfNiMBP6z6skRWINOq83CJh1LgcsO3XEDMHMq/ydmsEARAhShAEQRCESSA3OZRX7lvMgwXTiTDq+MOmCky9Nm83SxgNxa9Cdy0UPCp6oQSfIX4TBUEQBEGYFHKTQ/nBZZm8fN8izDYn979UhNXu8nazhAvhHITtv4P4PEi/zNutEQQPEaIEQRAEQZhUMmOC+MttORxp7uX7bxYjy4q3myScry9egt4GuORRkCRvt0YQPESIEgRBEARh0rk0K5ofr8li/WETf/q00tvNEc6Hwwrbfw9JSyG1wNutEYQRRIlzQRAEQRAmpfuWTaOyrZ+/fFrJ9MgArpkX7+0mCeei6Dkwm+DGZ0UvlOBzRE+UIAiCIAiTkiRJPH7tbBZOC+MHbx3iQH23t5sknK1BM3z2BExbASkXe7s1gnAKEaIEQRAEQZi0dBoVf78zl+ggPQ/8az/NPVZvN0k4G3v/AZYOuOR/vN0SQTgtEaIEQRAEQZjUwgJ0PHf3Amx2F/e9WITF7vR2k4QvY+uDXX+BtNWQuNDbrRGE0xIhShAEQRCESS8tOpC/3J5DmamP774mKvb5tD1PgbUbCn7s7ZYIwhmJECUIgiAIwpRQkBHF/1w5k40lrfx+Y7m3myOcjqULdv8VMtdCXI63WyMIZySq8wmCIAiCMGXce1EKlW1m/lZYzYwoI9fPT/B2k4Thdv8VBvtFL5Tg80RPlCAIgiAIU4YkSfz8mmwWp4bxw7cPs7+uy9tNEo4b6IA9f4fs6yA629utEYQvJUKUIAiCIAhTilat4qk7cokL8eOBl/bT2G3xdpMEgJ1/AqcV8n/k7ZYIwn8kQpQgCIIgCFNOaICOf969ALtL5r4XizAPiop9XtVvgr3PwJxbIDLd260RhP9IhChhSlMUhS31W3j64NMUtxV7uzmCIAjCOJoRZeRvd8ynss3Md/59AJeo2Oc9nz0BLgeseNjbLRGEsyIKSwhThkN2UNNTQ1lXGWVdZZR2lVLSUYLV5V548amDT/G93O9xW+ZtaNVaL7dWEARBGA/L0iL5f1fN5H/fP8pvPy7jR1dkebtJU09vI+x/HnLugLBUb7dGEM6KCFHCpGRxWKjorhgRmKq6q7DLdgAMGgNpoWnMCJ3BkY4jKCi4FBe/K/odfz/0dwoSC1iZtJKl8UvRq/VefjWCIAjCWPrKkhSq2sw8vb2G6VFGbs5L9HaTppbtvwNFgeU/8HZLBOGsiRAlTHg9th5Ku0o9Yamsq4y6vjpkRQYgWB9MZlgmt2fdTmZYJllhWSQHJaNWqSluK+b+jffjkB1oVBoenPcgVT1VbG3YygfVH+Cv8WdFwgpWpazioriL8Nf6e/nVCoIgCGPhf9fOpKZ9gEffPUxKeAALp4V5u0lTQ9cxOPAy5N4LIUnebo0gnDVJUcZ2/G9eXp5SVFQ0pvsQpgZFUTANmE4JTKYBk+c+sQGxnqCUGZZJVngW0f7RSJJ0xuctbiumqLWIvOg85kXNA8DhcrDXtJdNdZvYUr+F7sFu/NR+LEtYxsqklSxPWI5RZxzz1ywIgiCMn16Lg+ue2kn3gJ33H7yYpHDxxdmYe+9bcORteKgYgmK93RphCpMkab+iKHlnfX8RogRf5JJd1PbVuoNSp3tIXll3Gb2DvQCoJBUpQSknAlN4JpmhmYT4hYx6W5yyky9av2BT3SY212+mw9qBTqVjadxSViavJD8xn2B98KjvVxAEQRh/xzoGuPbJnUQF6nnnW0sJ9BNzZMdMRxU8uQAWfRMu/6W3WyNMcSJECRPG8R6guZFzMWgMIwJTRXcFNpcNAJ1KR1po2ojAlB6ajkFjGPc2y4rMwfaDbKzdyOb6zZgGTGgkDYtiF7EqeRUFSQWE+YkhIIIgCBPZruoOvvLsXi5Oi+DZuxegVp15NINwAd76GpSvh+8cAmOkt1sjTHEiRAk+S1EU2q3tVHZXUthQyBsVb3jmLR0XqA109yoNBaaMsAymBU9Dq/K9bwIVReFIxxE21W9iU+0mGs2NqCQVC6IXsDJ5JZcmXUqkv/hQEARBmIhe+byOR989wtcunsZP1s70dnMmn9YSeGopXPxdWPlTb7dGEESIEnyDxWGhqqeKyu5KKrorqOxxnx8fjjechMQV067g2znfJt4Y/6Xzl3yVoiiUd5d7eqiO9R5DQiInKoeVyStZlbyKmIAYbzdTEARBOAc/+/Aoz++s5VfXz+a2haLowah6/S6o3grfPQT+YgSH4H0iRAnjyiW7aOhv8ISk46Gpsb8RBffvlr/GnxmhM0gPTSctJI300HQGXYN8d+t3ccgOtCotz6x+xlPUYTKo7qlmY91GNtdtpqK7AoDZEbNZlbyKlckrSQwU5XMFQRB8ndMl87UXi9hZ1cG/vraIJdPDvd2kyaHlIDy9HFY8AgU/9nZrBAEYgxAlSVIw8BqgBgaAW4CngJnAR4qiPP5ljxchavLosnVR2V3pCUoV3RVU91R75i6pJBVJgUnusBSa5jmPN8ajklSnPN/pquJNRnV9dWyq28Smuk2UdJYAkBmW6QlUqcFiYUFBEARf1WdzcP3fdtFhHuS9b11ESkSAt5s08b16C9Tvge8cBMPoF4QShPMxFiHqW0CloiibJEl6CtgNXKIoyj2SJD0H/EpRlMozPV6EqIln0DVITU/NiJ6lyp5KOqwdnvuE+YWdCEohaaSHpTM9eDp+Gj8vttz3NZmb2Fy3mU11mzjYfhCAGSEzWJm8kqTAJFotrZM+VAqCIEw0dZ3uin2hATre/dZFBBt8b57uhNFYBP+8FC75CSz/b2+3RhA8xnQ4nyRJbwFBwJ8URVkvSdKtgEFRlOdPut8DwAMASUlJuXV1dWe9D2HsDe8BivSPpKKrYsRwvLq+OlyKCwC9Wk9qcOopvUsRhggvv4qJzzRg4tP6T9lct5mi1hNfNGhVWp697FlyonK82DpBEARhuM9rOrnz2c9ZnBrO8/csQKM+dYSFcBb+dZ17ON93DoFerLco+I4xC1GSJC0BHgdqgb8oinJQkqTVwHxFUX59pseJnijf8mndp3x/2/c9IWm4eGP8KWEpKTAJjUrjhZZOLX/+4s88e/hZzzyyEH0ID81/iKtSrxK9e4IgCD7i9X31PPL2Ye5ekszPrpnl7eb8R6aaXpoquolPDyUm1QfWM6zbBc+vgVWPwUUPjfvuW6p7aK7s8Z2fh+BTzjVEndXRsSRJYcD/ATcA3wOOL9BjBMRXMT5OURQOtB3g5dKX2Vy32XOgLiFRkFTAvdn3khaaRoBWjPP2lhUJK3i55GUcsgOVpCJEH8LPd/+cvx74K7dl3satGbeOyULCgiAIwtm7ZUESVW1mnvnsGDOijNy1JMXbTTojU00v7z1xAJdLRqWWuOSuTBKzwvEzalF5Y90rRYEtvwBjNCy4b9Sf3m5zMtAz6DmZewYZ6LG7r/cO0tdhxdrvcN9ZgsTMUGKmhxAcaSAkyp/gKAN+AWKYpnD2/mOIkiRJB7wJ/EhRlDpJkvYDFwN7gLlA+dg2UThfdpedj2s/5pXSVyjpLCFQF8gV065gc/1mnLITrUrLvdn3ivk3PmBe1DyeWf2MZ5jl3Mi5FLUW8fyR53my+EmePfws1864lq9kf0VU9hMEQfCiH67Jorp9gJ9+WMK0CCMXp/ne8HZFUSjaUIvL6V6LUXYqbH6+1H2jBAajFkOgDkOgDv/AE5cNQ5f9g05c1urVo7P0yLFtULcD1vwWdP5n/TBZVrD22YdC0fCANPJkt506wkZn0BAQoscYosMYqj8RohRore2noawbhg3I0gdo3IEq0kDw0LkIWMKZnE1hiW8CvwQODm16Hndv1KfAGmCxoiinLv4zRAznG38d1g7eKH+DN8rfoNPWSWpwKndk3cHa1LX4a/2nTFW8yaKqu4oXS15kXc06ZEXm0qRLuTf7XmZHzvZ20wRBEKakfpuDG5/aTUuvlXcfvIjpkb4zt8dmdvDpS6XUHurgePaR1BJ5a1LQ+2ux9tuHTg6s/XYsQ5ftVudpn0+tVWEI1OI/LGi5Q9ZJwStQh1+gFvVp5oqZqntoevUJ4lX7iXn4bdC6h6kf7z0aEYq6h673unuRLH12FHnksapKJeEfrBsKSHr8h84Dhk7HL2v16hNtqOnl/T+6e+bUahXX/FcOEYlG+jps9LZZ6G230tNmdV9us9LfbTslYAVH+hMSJQLWZDUu60RJkhQKrAK2K4pi+rL7ihA1fo52HuWVklf4uPZjHLKDZfHLuDPrTpbELZmQC9gKI7VZ2ni19FXeKH+Dfkc/86Pmc++se1mesPy0JeQFQRCEsdPQZeHaJ3cSZNDy7reWEuKv83aTaKroZtNzJVjNdpZeN4OolMCzngPkcshYzXYsfacGrNMFL9l5+uNHvb/mRNgK1KEoCscOtqMoCpIkEZ4YhMvuwtwziOM0vUd6f3fvUcBQSAo4KSAFhOjxD9QhnceQxHOZI+Z0uM4vYA31YomANfGIxXanGKfs5NP6T3m19FW+aPsCf40/18y4htszbyclOMXbzRPGwIBjgHcq3+FfJf+iZaCFacHTuHvm3aydvha9Wu/t5gmCIEwZRbVd3P7M5+SlhPLiVxei9VLFPllWKProGEXrawmKNHDZfbOITAocs/0pioLd5sLadyJcWYYFLcvx7b0W+jssOOXjs0cUjEEqoqdHjAhFw0PS8N4jX3WhAcvlkLH0DZKQGSYKXPgQEaKmiN7BXt6qeIvXyl/DNGAi3hjP7Zm3c13adQTqxu6NU/AdTtnJxtqNvHD0BUq7Sgn3C+f2rNu5JeMWgvXiTVkQBGE8vL2/ke+/eZDbFyXxi2tnjfvID3O3jU3PldBc2UPGohiW35aOzs+LVXVlGaq3wP7noXwDpsEZvN/1M1yoUePimtW1xFz/Te+1b4y5HDK9HdazClgqtcTab88lMSvMew0WPESImuSqe6p5pfQVPqz+EJvLxsKYhdyRdQcrElagVvn+tzfC6FMUhb2mvbxw9AV2NO3AoDFw3YzruGvmXSQEJni7eYIgCJPebz4u46nCan561UzuuWjauO332MF2Pn2pFJdTYcVt6WQujh23fZ/C3AYH/gX7X4SeOvCPgJw7IW4epteeoMmWTrxfBTEP/AESF3qvnV7kcsjsereKQ1sbPWFKrZGYXZDInIIEAsPEkibeJELUJCQrMjuadvByycvsbtmNXq3nytQruT3zdjLCMrzdPMGHVHZX8sLRF1h/bD2yIrMqeRX3ZN/DrAjfX89EEARhopJlhW+8vJ/Npa08d88C8jOixnR/LofMrnfcB+MRiUYuu28WIdFnX/Fu1Mgy1G6HouehbB3ITkhZBnn3QuZa0AwNMW/YC7WfuW+bogHquOEFLlQqiZgZITRX9AAwIzeKeSsTiUoO8nIrpyYRoiaRAccA71W9x6ulr1LfX0+UIYpbM2/lxvQbCfUL9XbzBB/WOtDKK2Wv8Gb5m5gdZvKi87gn+x6WJSwTRSgEQRDGwMCgkxv/vpvGLgvvPriUGVFjM7S+2zTAxmeP0tFgZu4liSy5bjpq7Ti/rw90QvErsP8F6KoGQyjMuwNy74GItPFtywR0coGLvk4rh7Y2UrKjGYfNRVxaCPNWJpIyO+K8CmgI50eEqEmgob+BV0tf5b2q9zA7zMyJnMOdWXeyMnklWpWo8iKcPbPdzNuVb/Ny6cuYBkykBqdyT/Y9XJl6JTq19ytJnYkowy8IwkTU1GPlmr/uRC3BTXmJFGRGkZs8el96lu1pYdu/K9BoVFxydxbT5ozjGlWKAvW7oeg5KHkfXHZIXAx5X4WZ13jKlgvnb9DqpHRnMwe3NGDuGiQ4ysC8SxPJWBKLViembIw1EaImqOPzWl4ufZltDdtQS2pWp6zmzqw7xXpAwgVzyA4+qf2EF468QHl3ORGGCG7PvJ2bM272mSIUiqJgdVrZ1byLR7Y/glN2olPreGb1MyJICYIwYfx7bz0/eucwAH5aFa/ct/iCg5Td5mTbv8up+LyVuLQQVn01G2PoOFVjtXbDwdfd4amjHPTBMPdWd69T9MzxacMosRw4gGXvPvwXLsA/J8fbzTkj2SVTfaCd4k31tNX1ow/QMGt5PLPzEwgIFlV4x4oIUROMzWnjo5qPeLn0Zap6qgjVh3JTxk3cknELUf5jO6ZamHoURWFPyx5ePPoiO5t3YtAYuCHtBu6ceSfxxvhR3U+/o58eWw89g8NOJ13vHeyle7CbXlsvPYM92GX7Kc+1IGYBP1n8E6YFj99kbUEQhPP15NYqfv9JOQogAf99WQYPFsw47+drq+tj4z+P0tdhZcHaaeSuSUE11kO8FAUai9zB6eg74LRBfK671yn7etB5Yf7VGSguF7LVhmwZQLFYkC0WZKvVfT5w/PIAg1VV9Lz5FrhcSDodSS8879NBCtyfpS3VvRzc3EDNwXZUaon0BdHMvTSJiATfWeB5shAhaoIwDZh4vfx13qp4i57BHjJCM7gj6w6uSL1CrPUjjIvyrnJePPoiG45tQEFhdfJqlsQvodPaOWIYnVN20mfvGxGCjocfz2VbN72DvSPCkUs5dRFFALWkJlgfTLA+mFB9KMH6YEL0IYT4hRCiD6Hf3s+LR1/EKTuRkFCG/s0Kn8Xa6Wu5POVywg3h4/mjEgRBOGv767q54597sDlkAJ65K49V2dHn/DyKonBoSyO73qnCP0jHqq/OJC5tjOdD2/rg8BvuQhGtR0BnhNk3uQtFxM4976e1HDiA5fO9GObOQZ+W5g44FguyZSjsWAaQLRaUU8KPxXNSrEPbTwpKis12Xm3SJiQQescdGPNXoJ/m+1/S9bRZOLSlkdJdzTjtMgmZocxblUTSzLBxL6s/WYkQdRq+Mr+iuK2YD6o/oL6vnqLWIhQUChILuCPrDvKi88QfgeAVpgETr5S+wmtlr2FzuT+MJCSi/KOwOq302fvO+FidSkeIPoRgv5MC0fGTX8iI68H6YAJ1gf+xuMXwv9k4Yxwbjm1gXc06yrrKUEtqlsYt5arpV5GfmI9BYxjVn4cgnC1f+WwRfM/+um4+PtLCi7tqyc+I4h9fOevjMgCsZjtbXiyl9nAn0+ZGcMldWfgZx3BOdPMBd3A6/BY4BiBmjrvXafaNoL+wAhk9739Ayw9/6O7dOgeSvz8qf39UBoP73HNyX5f8/VEZ/E97m8rfH8lgQOUfgCrAfX2wooKGb3wTxW4HSUIbF4ejoQEAXUoKxvx8jAUF+M/PQdL67vxz24CDo581cXhrIwO9dkJjA5i3MpH0hdFotGLe1IUQIeoke1v28vXNX8cpO1FJKpbGLSVYH4wsy7gUFwoKLtmFrLivy8jIsnziuiJ7TsOvf9ltp7tul+3YXSeGKq1JWcND8x8S6/gIPuPJA0/y9KGnUYYWr0gPTSc3OndkKDopGBk0hnEN/5XdlXxU8xEfHfsI04AJf40/K5NXctX0q1gQvUCslSaMGafs5FjvMUq7SintLGWfaR/l3eUAaCQNv1z2S9ZMW+PlVgq+5u/bqvn1hjL+fud8Lp91dms4NZV3s+m5o1gHHFx0Qxqz8+PH5n120AxH3nYP2WspBo0BZt/gDk9x8+EC96nIMl0vvkTb737nLoUOIEkYCwoIXL1qKOwEnBJ+VAYDksGApBr9ioMnz4myNzZh3laIeWshls8/R3E4UAUFYbz4YowF+RiXLUMdEjLq7RgNLqdMVVErBzY30NloxhCoZXZ+ArOWx2MI9N3CUb5MhKghDf0NvFn+Jq+Wvcqga9CzPVAbSLA+GLVKjYSEWlKjUqlQS2e4rlKjkoauS0O3D11XSSrPafj10912tPMoxW3FKCioJTXfzvk2982+b9x/LoJwJsVtxdy/8X4csgOtSuvTBR1kRWZ/637W1axjY+1GzA4zUf5RXDntSq5MvVKsnyZckEHXIFXdVZR0lVDWWUZpVykV3RWezxI/tR8hfiGYBkwjHpcSlMKKhBWsSFxBTlQOGpXGG80XfIjTJXP1X3fSYR5k0/dWEGw4cw+H7JLZ91EtRRtqCYnyZ/V92UQmjkGZdNMR2P+8u1iEvR+iZkLuvTDnZjCMTmBwtLTQ/KMfY9mzB0NeHrbDh1GcTiStlqTnn/PJuUgu8wADu3dhLizEXLgNV2cnqFQY5ucQWFCAMT8fXWqqz40aUhSFpvJuijc3UHekE7VWRcbiGOZdmkhoTIC3mzehTOkQdXxR2tfKXmNH0w5Ukor50fMpbitGVmSvHhhOpANUYeqaiMOTbE4b2xq3sa56HTuaduBUnKSHprM2dS1rpq0hJiDG200UfNiAY4DyrnJPD1NpVyk1PTU4FSfg/uItMzyTrLAsssKzyArLIiUohcMdhz3v6RqVhlsybqG6p5q9pr04ZAdBuiCWJSwjPyGfi+IvIlA3NmsGCb7vcGMv1zy5g1sXJvHL605fbbe/y8am547SUtVL5pIYlt2Sjs5vFEL48UVuExZCb4N7yF7jXlDrIfs6d69T4sIL7nUarvejjzD97OcoTicxP/4RwTfcgLW4eEJUxTtOkWVshw/TX+jupRosKwNAm5REYEE+xvx8/HNzkXS+1ePT1TLAwS0NlO8x4XLIJM8OZ97KJOLTQ3wu/PmiKRmiegd7ebfyXV4vf51GcyMRhghuTL+RG9JuICYgxmcODH2lHYIwWXXbuvm49mPW1azjUPshJCQWxi5kbepaViatxKgT1Yymsh5bD6VdpZR1lXkCU11fnWcIa5hfGFnhWcwMm0lWeBaZYZkkGBPOePBxuvf0AccAu5p3UdhQyGeNn9E92I1G0pAbncuKxBXkJ+STGJQ4bq9Z8A2PryvhnzuO8cbXl7BwWtiI22qK29nyUimyS2HF7RlkLBqlL34a9sKLa8Fph6HfccLT3EUi5t4G/mFf+vBz5errw/Tzx+hbtw7D3LnE/fY36JKTR3Uf3uJoacFcWEh/YSGW3XtQ7HZURiMBF19MYEE+AcuXowkd46If58Dab+fI9iYOFzZi7XcQkWhk3qWJzMiLRq0Z54WZJ5ApFaKOdh7ltbLX2HBsA4OuQeZHzee2zNu4NOlStGrfnRQoCMLYq++rZ13NOtbVrKOhvwE/tR8FiQWsnb6WJXFLxMLVk1y7pZ3SrlJKOkso7XQHp+aBZs/tsQGxI3qXssKziDREjuq3tS7ZxeGOw2xt2Mq2hm1U91YDMD14ujtQJeYzJ2KOmMs3BVjsTlb/cTt6jYr131mGXqPG6XCx6+1qDhc2EpkUyOr7sgmJGqXS4S4HvHIT1Gw9sW3+V+Cqv4xqr9NxA5/vpfmHP8TZ1kbEg98i4oEHkDSTczirbLEwsGcP5q1b6S8sxNXe4R72N28exvx8Agvy0c2Y4RM9P06Hi4q9rRRvbqC7ZYCAYB2zCxLIXhaPX4D4DDzZpA9Rg65BPqn9hNfKXuNwx2EMGgNrU9dya+atpIemj9p+BEGYHBRF4WD7QdbVrOOT2k/oGewhzC+My1Mu56rpV5Ednu0TH3bC2RveAzQ3ci5N5qYRw/FKO0vptHV67p8SlEJWWNaJYXlhWYT4jf9k8Ya+BrY1bqOwoZD9rftxKk5C9aHuYX+J+SyNW0qAVsxhmKy2VbRz93N7eejSNO6dHc8n/zxKZ6OZuSsTWXLt9NHrIWgsgg8egrajIKkACdQ6uPsD99C9USTb7bT/6c90Pf88uqQk4n73Wwxz5ozqPnyZIsvYjpa451Ft3YqtpARwl0835g8N+1u4AJWXh/0pikJ9SRcHN9fTUNqNRqcia2kccWnB9LZbiU8PJSY12Ktt9AWTNkQ1mZt4o/wN3q18l+7BblKCUrg181aunn71hBlr3lxRSsPRwyRmzyYuPWtM9yXLLlxOJy6HA9npdF92OnE5HbRWV9LRUM/0vEXEZ4xtOwTBlzhcDnY07WBdzToKGwqxy3ZSglK4MvVK1qauFdUyfZiiKHTaOtlSv4Vf7/01DtmBChV+Gj8sTgvgXoNsesh0MsMymRk+k6ywLDLCMnwymPTZ+9jVtIvCRvewvz57H1qVlgUxC8hPzCc/IZ9Y49lVcxMmju/++wuOFbVzuV2PTq/m0ruzSJkdMTpPPtgPnz4Ge/8BQXFwxe8hIMI9Jypl2agHKFtFBc0/eJjB8nJCbr2F6IcfRuXvO4vweoOjtRVz4TbMW7cysHs3yuAgKn9/Ai6+2B2qVixHEx5+SpXA8dTRaObg5nrK95pQjhdNVEksumqau4dqLEvp+7hJFaJkRWZX8y5eL3udbY3bkCSJgsQCbs28lUUxiybMt8eDFgtV+3az8R//h+xyoVKpmLv6Coyh4bgcDk+4GR52ZKfjxGXXiQB0/P5nvO/QuXL8L+M/CItPICY1jbD4RMISEgmPTyQkOhaVWgwvESa3Pnsfm+s2s65mHftM+wDIicphbepaLku5jGC9+FZuvNlddprMTTT0N9DY3+g+NzfS2N9Ik7kJq9N6ymNmRczi+rTryQrLIi00bUIuVu6UnRxoO8C2hm0UNhZS11cHuJcZWJGwgoLEArIjsv/j+mqCb7NbnWx8qYS6Ax10G1V858eLCQzzG50nL/8YPvo+9DXBwvvhkp+AX9DoPPdJFFmm66WXaH/ij6gCA4l9/DECCwrGZF8TmWy1uof9FW7DXFiIs7UVJAnd9OnYa2tBlpF0Oq9VK9z9XjVffFx3yvbweCPx6SHEp4cSlxYypULVpAhRvYO9vFf1Hm+Uv0F9fz1hfmHckHYDN2fc7JOVtmSXi/7OdnpaTfS2mehtNdHT1kpfm/vc1n/mxUoBJJUKtUaLWqNBpdGgHjqphraN3D50P7UGtVZ72vueuP/Jz6ml5kARFXt2eBa9C46OxeWwY+46MfRFpdYQEhNLeHwiYfGJhMcnuENWXAJav1F6wxcEH9JiZgkoKAAAIABJREFUbuGjYx+xrnod1b3VaFQalscvZ3bEbJyKk8Wxi0UxmFGgKAq9g72ecHRyWGodaPUUeQAwaAzEG+NJDEwkITCBxMBE7C47f/niL7gU16StdHqs95gnUB1oO4CsyEQYIliesJwVCStYHLsYf+3U/sZ/ommr6+OTfx6lv9NGwPwwflrZyM+vzeauJSkX9sT9rbDhYSh5DyKz4Or/g8QFo9Lm03GYTDT/6EdYdu/BeMklxD72czTh4WO2v8lCURQGS0vp37qVnjfedAeqIdrERIKvuQb/vFwMc+eiMozPAvKmml7e/+MBXC4ZlVrFxTelYRtw0FzRTUtVL06HDNJpQtUoz6XaX9fNjsp2Lk6LJDfZu8U5JnSIKu0s5bXy11hfsx6by0ZOVA63ZtzKquRVXi0UoSgKNnP/UDgy0dvW6glLvW0m+jraUeQTPT8qtZqgyCiCo2IIiY4hOCoGl9PJnndeQ3a5UKs1XPPw/5KQOROVRo1qHCcVN1eU8uZjj+JyOlFrNNz0k18Ql57FoMVCV3MDXU2NdDU10Dl03tPaMuK1BUZEDgtX7mAVlpCIf5D41l6Y+BRFoayrjHU163i/6n167b0AqFBxY/qNXD3jamaGzxRFKb6EU3bSMtBySk/S8etmh3nE/SMMEe6QZEwYEZYSAhMI9ws/7YiDqVTptMfWw2dNn7GtcRs7m3ZidpjRq/Usil3kXpMqYQUtAy1T5ucx0SiywsEtDex+txr/YB2rv5pNzPRgvvLcXg7U97Dpe8uJDT6Pg2ZFgS9egk0/AYcNVvwAln4HNGM396Zv/XpafvozFKeT6B8+QshNN02YEUG+xHLgAPX33IvicIAkoU1IwFFf7/4/1WgwZGdjyMvFPzcP/9z5qIPH7vjKVNNLU0X3KXOiXE6Ztto+mip6aKroxlQ9+qGqz+bg2c9q+L8tVcgK6DUqXr1/sVeD1IQLUXaXnU9qP+H18tc52H4Qg8bAFdOu4NbMW8kMyxzTtg3ndDjoa28b1pM0FJLaW+ltNWG3Wkbc3z84hODIaIKHQlJwdDQhUe7LxvDw0waj8ZwT9WXOpR1Oh4MeUzNdzY10NTbQ2TQUtJobcdpPLGLsFxg0osfqeNAKiogck1XHBWGsPX3waZ4sfnJEzwi4e0fmR80nLyaPhTELyQrPmjKh6nh4yQ7PJtQvdGRP0tB5y0ALLsXleYxWpSXeGH8iHA0LS/HGeNGjcg4cLgf72/ZT2FBIYUMhTeYmACTcB7JalZZnL3tWBCkfYKrppfZQB43l3bQe6yN1XiQFd2V6DjjrOy2s/tM2lqVF8o+7cs8tjHRUwoffhbodkHwxXPVniJgxRq9kqHT5Y4/T9+GH+M2dQ/xvfztpSpd7y8lzolz9/VgPHMCyrwjL/v3uxYmHQpY+LQ3/vDx3T1VuHtroqHFv75eFqogEI/FpocSlh/zHUNVpHmRzaSsbjpjYWdWBw3Xi81UlwfdXZ/Bgwdj9Lv8nEyZEtZhbeKPiDd6pfIcuWxfJQcncknEL18y4hiDd6I7jPR4aIpJS8DMGuoNSm4neVnePUk+byT2cbdjPQqPVERQV7elJCo6KHgpL7ss6v/HpbvVViizT39kxFKoahp03jhi+qNHrCYtNICzefToerkJj42itqfKJUCkIp3PyAtl/WPEHbC4b+0z7KGotoqqnCgB/jT850TksiF7AgpgFzAyfiUY1OUr7Wp1WqrqrKOsuY2fTTrbUbzklVAKE6EM8AWl4T1JiYCJR/lFiLs8YUBSF6p5qflf0O3Y17/JsN2qNXJl6JQWJBSyIWYBO7VuLgU4FzVU9vP/HA8hDB4jzViWy9PpTS17/Y3s1v1xfxlN3zGfN7LMoIuK0w84/w/bfgdYPVj8OOXeNScny40aULv/WN4n4+tcnbelyXyLbbFgPHcK6f787WBUXo1jcX+Zrk5Lwz83FPy8X/7w8tElJ494j6HLItNb10VzRTVNFDy3VvbjOEKp6XS4+OWpiw2ETnx/rRFYgIdTAmlkxTIsw8vN1R3E4ZbQaFa/cJ3qiRhgeomRFZk/LHl4re41tjdsAWJGwglszb2Vx7OIL/qC1Wy1DQ+2Ghtu1tdJaU0VzZdmIgHScMTTsRE/S0NC7oCh3j1JASKjoQTlPlr5euoZ6rDqbGty9WE0N9LW3nbiTJHn+T1QaDWu/8zBpC5d6qcWCcHpfNmys09rJ/tb9XxqqjvdU+XqoUhSFDmsH5d3llHWVUdFVQVl3GXV9dchDRWp0Kh122Q64ez7WTFvDPdn3kBCYMGEqpE5Gx8O+XbajklTMjZhLSVcJVqcVf40/F8VfREFiAcvil3mlrPtU4nLKlH9uYve71djMDsD9UbfomlRyL0855f5Ol8y1f9tJa98gm7+3gmDDl/RoN+x1ly1vL4Xs6+Dy30Bg9Bi9kqHS5X/+M13PDZUu/+1vMMydO2b7E76c4nRiKy3DUlSEZX8R1qL9uHp6AFBHRrh7qnLdvVX69PRxP349OVQ1V/UgO91fubWpZRo0Mo4wLfPmx3D5/Diy44I8wW9/XTd7ajpZnBou5kSdLCU7RXl106vU9NTwevnr1PbVEuYXxvVp13NT+k3EGePO+rlcTif9He3DQpJpRGCynlTAQWcwoNXpGeh1/6IhScxcXsDCa24iKDIKrW7iVXGayBw2mydQHd66kYajh0fcHhITS1L2XJJmzyUxe46YZyVMKJ3WTopai9yhylTkWVjVX+PP/Oj5LIhZwILoBV4PVU7ZSW1vLeXd5ZR3lXuCU5ety3OfeGM86aHpZIZlkhGaQUZYBh3WjhE9c5OxoMNEdXLYtzlt7DXt9Szy225tRyWpyInKoSCxgPzEfJKDxHCs0WK3OSnZ0Uzx5gYGegYJjjLQ32lDURTUahXX/FfOGdfgOdLUyzVP7uTmvER+df3sU+9g64NPfw77/glB8XDlHyDj8jF9PbaKCpoffoTBsjJCbrmF6EdE6XJfo8gy9poaLEX7sezfj6WoCGdLCwCqoCD8c3Lc86ry8jBkZyONwzpVVW1mPj7SwsdHTZQ29hHjUpHrZyBdpUXdZUd2Kid6qtJDiU8PIXZGCD2tltPOy/IGnwtRhmkGZcZP3eMb50TO4daMW7ks5bLTDjFQFAVLb8+wog2tnjlJve2t9Hd0jCjdrVKrCYqIcvcmeeYnRXsu+xkDaaksO20hBcG7hhe4UKnVzFm5ht42E40lh7Fb3WWMI5NShgLVXBKyZqEXb+LCBNJh7TjRUzUsVAVoA5gfNRSqYhaQGZY5ZqGq395PRXfFiLBU1V3l6VHSqrTMCJlBRlgGmWGZpIemkxGWccYh1VOpoMNkISsyJZ0lnkBV3l0OwLTgaeQn5JOfmM/cyLmox7HA0WRhMzs4VNjIoa0NDA44iU8PYf5lySTODKP1WN9ZHxj+cn0p/9hew+sPLGZR6rBKd2Xr3WXL+1tg0dfhkv8B/dj1+iqyTPe//kXbH55wly5/7DECLxGlyycKR1OTu6dqKFjZa2oAkPz8MMyd6xkCaJg3zxOKL2S9KkVRKGnp45MjJjYcMVHZ5i4alJMUwuXZMVw+K4bkcPcafS6HTGut+2+iqaIHU83Q8L8Tz4ZaLXHt93O9GqR8LkSFxxmVuQ9ncOmS63l00aOeIXc9bSb62lrpaTXR197qGYY3vFgBQEBomDsURUWfCEtDl41hpy/gcDJfKeggjHS6/xfZ5aK1por6IwepP3KQpvISXA4HkkpFzPQ0kmbNI2nWHOLSs9B4eQVwQTgXHdYOilqLKDK5e6tqet0fcKMRqhRFoWWghbKushGB6XjhAYBQfSgZYRmenqXMsExSglOmTFEMwa3Z3OwpTLGvdR9O2UmoPpRlCcsoSCxgadxSUezjPzB32yje3MDRHc04B12kzIkg9/Lk8z74s9pdrP7TNrRqFesfWoafrR3W/wBKP4CobHfZ8oTcUX4VI40oXV5QQOzjj4nS5ROcs7PT00tlLdqPrawMZBk0GvxmzkSbmIh540YUpxNJoyH8m99Al5CA4nCgOJzuc+fxc4dne1tXP/WtfTR29GOz2tAqMlF+KmKNWqIMavTIJz125HPgcOJ0KvTqYqiJvYTu4PShKR4uchcFsPir3pva4XMhKjEsWHlo1cUYoyORLfZT1kzSGfyHijZEnyjgEB1DcGQMQVFiyN1U57Tbaa4opf7IIeqPFGOqrkSRZdRaLfEZWSTNmkdi9hxipqeJBYKFCeVMocqoNbqH/w0VqsgIy+BIxxFPD9DM8JlU91S7A9OwIXn99n7APV8pOSh5RO9SZlgmkYZIUY5YGKHf3s/O5p0UNhSyvXE7/fZ+tCoti2IXUZBYwPKE5T65NqO3dJsGOLCxnvLPTSgKpC+IJueyJMLjjBf83J9VtvOVZ/fw9MyjrG76GzhtkP8ILH0IxniJl74NG9yly+12on/0Q1G6fJJymc3uCoBF+93zqr444A5VZ0mRJJySGodKjVOlRqXVotPr8PP3Q6PTImm1SBrNiXOdFjQaJK1u5HatFknrPm8ubWOP/jJkSY1KcVGQ1Ubm974yhj+FL+eDISpE+e6qi93zXWbN9RRxOB6c/IyB4o9VOGuDFguNpUdoOHqQ+sMHaa+vBdxhPGHmLM+cqoiEJFEYRJhQOqwdnkC1r3Ufx3qPAWBQGxiUB5EVGQkJFSpcuEuIGzQG0kLTyAzNdPcyhWWQFpImehKEc+aQHRS3FbO1YSuFDYU09DcAMDN8pmfYX2ZY5pT8vG6r6+OLj+uoLm5HrVEx86I45q1MJChiFKv0tldQ/dzXmG49xEDcUgJu+CuETx+95z8NV38/psceo++DodLlv/kNupSUMd2n4DsG9u6l4b77PT1Rsb/4BYY5s5G0WhySmqKmfjZXdLCxoos2qwutVsPy9EjWzIrh0qzoLy+EcpYsBw5w+P/7GV0B0wgbOMbs//t/5zyscDT5ZIj67ysvEXORhDFh6eul4eghz/C/HpN7YqUhKJik7DkkzZrrDu/RMVPyw1+YuI6HqpdKXuJwx4kiLAuiF3BL5i1khGaQGJgo5rIIo05RFI71HvMEqoPtB1FQiAmIYUXCiilRPl1RFJoqevji41oaSrvRGTTMXhHPnEsS8Q8axdfttMOOP8Jnv0fW+vPzwds5GHElb33zItSqsfvMsuzbR9Mjj+BsbSPim98k4huidPlUNHxOlGrWHLZXtPPxERObS1vpszkJ0KkpyIxizaxY8jMiCdCP/u/IhczLGm0+F6KyZkxXPl2/TgQoYVz0dbQNDf1zh6qBbnfFscCISJJmzSV5lrvynzFMjPUWJoaT16sSVfGE8dZp7WR743YKGwrZ3bL7tOXTa/tqJ0XREUVWOHaog/0f19FW24chSMe8SxOZtTwenWGUDyDrP4cPH4L2Mph1A1z+a96ttPNfrx/kZ1dnc/fSlNHdH+7S5R1/+Qudzz6HNimR+N/+VpQun8J2VHXw+t56OgfsFDf0YLG7CDZoWZkVzZpZMVycFoGfdup8UedzIepMi+0KwlhTFIWu5kYahkJVw9FD2Abc1WPC4hNJmuXuqdLo9LQdqxaFRwSfJariCb7idOXTJU70mGhVWv552T/JifLuN8rnyuWSqdzbyhef1NFtshAU4UfO6mQyl8SgGe2DSFvvUNnyZyE4Aa58AtJXA+7Prbuf38f+2i42fW8FcSGjN2RwsLKSph887C5dfvPN7tLlAQGj9vzCxPLMZzX84qNSz/VVM6O5a3EyS6aHo1VPzekQIkQJwhnIsov22mPUDw3/ayw9gnNweDVIiYikZIKjYjAEBmEICnKfDz8NbdP7B4jhgYIgTGmyIlPaWcoT+59gr2mvZ7terWdp3FIWxixkQcwC0kLTUEm+eVDmsLvcazxtqsfcPUh4vJH5lycxY34UqrE4kCxdB+v/G8ytsOgbUPAo6EcWpmjosrD6j9u5aEY4z3wl74I/axRZpvvll2n7/R9QGY3EPv64KF0+xW2raOerL+zDJbszgFqC763O4MGCGV5umXeJECUIZ8nldPDpc09z+NOPPduCo2PQ6f2w9vdh7e/D5XSe9rEqtRo/Y+Bpwlbw6QNYUBBavd+XfhiKUvyCIExEx4ec2l121Co1S+OWUt1TTaO5EYAQfQgLYhaQF53HwpiFTA+Z7vUvoWwDDo5sa+TglkZsZgexM4KZf1kyybPCx6ZtfS3u8FS2DqJnw9V/hvgzly1/ZnsNv1hfypO3z+fKObHnv9uPP6btiT/iqK/HmJ/vLl0eEXHez3e+9td1s6emk8Wp4eQmh477/oUTPqts52svFhEX7EdLrw2nS0arUfHKfYun/P/NuYYoMYtQmLLUGi2z8i+l9LMtnsWYr/j29z0BRlEUHDarO1D19XmClec0bFtXU6Nn2/AFoUfsT6s9Y8+W3WrhwCcfobhcqLVaUYhFEIQJY17UPJ5Z/cwpQ05bzC3sNe1lr2kv+0z72FS3CYAwvzAWxCzw9FSlBKWMW6ga6Bmk+NMGjm5vwjHoInl2OPMvSyZuRsjY7FCWYf/zsPmn4LLDyp/Ckm//x7Ll916UwgcHm/l/Hxzl4hkRBPufXSU0xeXCeugQ5sJt9K1fj6PBXWURjYbwBx7wSoD6tLSV+18qQlZAq5L4+125XJoVPe7tEGBnVQf3vVjE9Egjr963iJqOARFuL8CY90TNS05Wdr33ntcrbgjCmYxmD5AiywxaLFj7e08btk7Z1tfrmac1XHhCErMvuYz4zJlEpaROuTWwfKlajyAIF05RFJrMTewz7fMEqzZLGwCRhkjyYty9VAtjFpIYmDjqoaqnzcKBjfWU7WlBcSnMyItm/mXJRCRc+BpPp9WwF46+C8e2Q+sRmLYC1v7xnMqWH2nq5Zond3JTbgK/vmHOGe/nMpsZ2LkL89atmLdvx9XVBWo12pgYHM3NoCigVhP50ENEfP2B0Xh1Z62otou7n9vLgN01YvvCaWGsmRXD5bNiiA0exVLxwhntqurgqy/uIyU8gFfvX0xYwOStrnm+fG443yw/g/JWRgZJLzwvDoYE4TRkl4vag1/wwRO/xOV0IkkShqBgLD3dAGj1fsSmZRCfOZP4jGxi0zPQ+U3eDx3LF19Qf889KA732hUJT/8d41LvrWAuCMLoUxSF+v56dy9Vi3tttA5rBwDR/tGeXqoFMQtICEw47/201/fzxcY6qve3oVKryFwaS86qJIIjL/A9VHaBpRPMbe75TQPtJy63lULNVjg+KmH5D9xzn84jGP5qfSlPb6/htQcWszj1RFVZe2Mj5i1bMRcWMrBvHzgcqIKDMS5bhjE/H+OyixmsqaH+3q+iOBxIWi1Jzz83bsdhiqLw/M5afrm+lAijji6LA5dLRqNWcc28OA419lJmci8QnpMUwppZMayZFUtimFjjbizsru7k3hf2khwWwKv3LyLcqPd2k3yST4aoN1NSCL7xRuIef2xM9yUIE9nJPWJ9He00l5fQVF5CU1mJe2FhRUFSqYhKSSU+YybxWdnEZ8wkIGTid8MPHjtG7/vv0/3Kq8j9/SNu06enY8jJwTBvHoZ5c9GljN/wH0EQzkLDXqj9DFKWQeLCc364oigc6zvGvhZ3T1VRaxFdNvcSFXEBce7hf7HunqqYgJgzPo+pppfG8m70Bg21hzuoP9qF1k/NrOXxzL00kYDgLzl4lGWwdp0UjFrd1z2Xh84tHSdC0nAaP1DrYbDXfV1SwyWPwrLvn/PPBMBqd3HZn7ajReHtFYHYd2ynf+tW7FXVAOhSUzHm5xNYkI8hJ+eUtZ680as/MOjkkbcPse5QCyuzovnDzXOpajOfMmyspt3MhiMmNhxp4UhTHwCz4oNYMyuWNbNiSI0co17CKebzmk7ueX4fCaEG/v3AYiJEgDqjMQlRkiRFA28pirJMkqR44HOgaujmmxRFaT/TY2cZDMqbKdNApSL60R8Tettt4uBHEM7DoGWAlooyT6hqqSzH6bADEBITS3xGtru3KnMmobHxE+LvzNXXR9/6DfS+9x7W4mJQqfCbM5vBoyUoLheSWk3Q1VfjbG3FevCgJ1ypQ0IwzJ17IljNniVK9QpTlqmml6aKbuLTQ4lJDR7/BjTsxfSP/6LJmkG8XwkxOdkQkgIaPah1p56fsk0PGh2o9cgqLU5Zi9OlobqvheK2co60l1HWXoltcBCNrCVaH8OMwHSmBaSSZEjBIPnjtLvobbdSua8V93GNhM5fTc7KJGYvCkDv6jo1GJnbYKBt2OV2UFynvj61DozREBAJxij3KSDKvc0YOXTb0HZ9IDTugxevds+BUuvg7g/OK1i6+vsZ2LGD6vc/ZnDXDoLtFtBo8M/LI7AgH2N+Prrk5Av+7xtNVW1mvvHyfmrazfz3ZRl8Y/l0VGexaHBDl4UNR1rYcMTEgfoeADKiA1kz291DlR5tnBCfab5m39BwyrgQA/++fzGRgSJAfZlRD1GSJIUC/waiFEWZL0nS9UC0oihPnc0O5sWGKzue+h3dG/Zi3raNkJtvJuZ/HkXSjeNYzAv8hkwQfJHL6aDtWA1NZUc9wcra7/42zxAYNDT8bybxmdlETUtFrTm7icljTXE6Gdi5k5733sP86RYUux192gyCr72WoLVXoY2OOu23p4osY6+uxlJcjLW4GOuBYuw1Ne4nVanQZ2TgnzNvqLdqHtrE0Z9XIQi+QlEUuloGKNnRzOGtjSiKe7RYXEYo/oE6JAkkSXKPIFNJI66fcbtKGnGZkx+jcp+P3A79u9/lcGMmMmpUyGT6b8Eg9eFUdDgVHS5FhxPdsOt6z+Xj211D12Uu5H3KBagACZBZEPIhC/1fAdlx6l1V2qEwFHmaMHRSMPILPveheOd53GGvq6N/61bMhduwFBWB04k6JISy5Nm8o0/hkf+9h6wZcefWlnHy0aEWHn7rIH5aNX+5LYeLZpxfEYuWXisfHzGx4YiJfbVdKAqkRgZ4hvxlxwWJ9/azcHw+WnSwH689sJioQD9vN8nnjUWICsL9jvS+oij5kiT9Frh0aNvHiqL8+DSPeQB4ACA3VpVb9PVglPQ1tO/opfPTagypYSTcnYcmUD/0xiQdf+BJlznD9nO43NcMh15zj1/W6OHuD0WQEiYlRVHobmmiqcwdqJrKj9JjagFAo9MTOyPdE6xi07PQ+4/v2HNbeQW9771H77oPcbV3oA4JIWjtWoKvvRa/7Jnn9aHo6unBeuiQO1QVF2MtPohssQCgDg8f6q2ah/+8efjNmoXKMHnnkgmTX3+XjcaybhrLumgs68bSZz/lPn5GLXp/DYoCKAqK7H5vUJQT52farsiKuxdn2G3nS5JArVWh0arQaCU0Wgm1BjQa0KgVNBoFjVpBrZbRqF3uk8qFWuVEo3KgUTnRSA7Ukh2NZEfDIBrsqCUbGmxIspU6pZtDcidFSjdFUh/GgSTWljyIWlYjq1ykzniWq+ZknwhDnh6kKDCEntccpdGmOJ1YDxygf2sh5q1bsR87BoBuxnQCCwow5udjmDePbpuLlU9sIynMn7e/uRT1WfTujBeHS+bXG8p4dscxcpJC+Nsd80etWERbv42NR1vZcKSFPTVduGSFxDCDZ8jfvMQQEahO44v6br7y7F6iAvXuABUkAtTZGLM5UZIkFQ6FqAKgCLAAm4HvKIpy6EyPy4tTK0UPGEFjAK2B3hoVLTu0qP0UEi8ZxC/s+JhihRPv2KN4+WQxs2HNbyFpiU+8gQrCWBro6fb0UjWVldBWW40iy0iSiojklKGeKvcpMCxi1NeqcnZ30/fhOnrfew9bSQloNBhXrCD42msIXLFi1HukFZeLwaoqrAeO91YdwF5X575Ro8EvI2PY3Kp5aOPjxAew4LNsAw6aKrqHglM3Pa3uLwgMgVoSMsNIyAxFb9Cw+fkSXC4ZtVrFNf+VM6pD+oYHreHhSqndjfLBd1AG2mnN+gkbPpuO7FRQaSSu+k4O8WnjO0/TKTv59Zbvsa20htj+NJqDqmgNrCU3Ope1qWtZlbyKYL0Xhjqehqu3F/NnOzAXFmL+7DPk3l7QaglYsMBdFKIgH11i4imPe+9AE999vZifXjWTey6a5oWWn6qtz8aDr37Bvtpu7lmawo+vyEKnGZuFlbsG7GwuaWX9kRZ2VnXgcCnEBvtx+VAPVW5yqE+FS285MBSgwo06XntgCTHBIkCdrfEIUXpFUQaHtj0B7FQU5e0zPS4vTqMUfStixJhg65GjNH7727h6eoj71S8JWrPmbNt77hr2Do1NHgQkd2+UwwIR6ZB7D8y9DfzDxm7/guBD7DYrLZXlQz1VJbRUlOEYtAHgHxKKta8XRVFQqzVc/f0fMy0n75xDhmK38/+zd97hcZT32r63d0mr3qxmyyruxrZsg41NL25A6ElIIZDyBQIJSU5ykkPaST2B5FACgZyQYiCEYjCYZkwzltyrJMtW79KupNX2MjPfH7NaSZZsXFTW9t6+9npnZ2ZnX0nenXnm+RXXhx/S9/IruD74AEIh9KWl4XC9a1EnTuznLdTbG3GpvHv24D1wAMnrBUCVkoxxblhUzZuLfsYMlLpYzHiMySEUEGivc9BSJbtN3U1OJAnUOhVZhQlkF1uZUpJIYqZp2OdyQnOihCB88Gv46H/Amgc3PAVZF0x+Xhbhpr9vfYmgGEKtVLN62lp2duykob8BjVLDiikruLbgWpZnLUfzKX2axhp/Xb0smrZswbN7NwgCKqsV88UXY165EtOFS1GZT1xIQZIk7vi/Hexq6OGd+y8mM2FynfWKOjvfWL8Htz/Er26Yxdq5WRP23g5vkM1VnWw62MEHNd0EQiIpFh1Xzkjj6pkZlOUnolaNFHO7O3ezq3MXC9MXRvqZnUvsa+7js09VkGjW8txdi2Pl40+RiRBR7wO3Ag5gO3CDJEmHj/e6BcVTpJ3vvDgihC5ks9H8KJX3AAAgAElEQVRyz714d+8m6at3k3LPPSiU43P3YlhsctoMOPSK3PyuZYec0Fq6VhZUuUtj7lSM8wpREOhurKe1+hAHNr+NraVx2Ha9yYw1K5vEzPAjawqJmdkkpKUP610lSRK+Q5U4XnmF/o0bEfr6UCUnE796tRyuVzR9on+04yKFQvhrauTcqrBjFWlIqdGgLymJ5Fah0RCorYv1q4oxLoiiRHeTMxKe137UgRASUSoVpBXERdymtLw4VON0d/+U6KmHl74inzvn3g5X/1oupBBF7O3aO6zpryRJVNorea3uNTbVb6LH10O8Lp4rc69k9dTVzEmZMy5utGfHDvo2vIroduOvrIw44rrp0zGvXIl5xcUYZs9GcYo9AJt7PFzx0IcsnZrEU3ec+k2usUCSJP78UR2/fvMwuYlGHv/sBRSlT97/A5c/xJbqLt482MF71V14gwJWo4ZLShKYke9CY2jnSN9hdnfuptE5eI5LMaSQYkzBorUQp40bfOjisGgs8hjeNnSfiRbgJ8v+lj5uf6oCq1EWUJMtss9GJiqc73EgADwpSdIjJ3rdggULpJ07d466TQwE6PjpT3H8+0XMK1eS+dvffOqdmDGl4yDsfgb2PS+XI00qHHSnTEmf+vIYMc4l2mqqeOFnP0QIhVAqlcy5/BqEUJCe1hZ62lpwh/tWAShVahLSM7AmpWB0utBW16BrbMEsgXXlShLWrcN04YUjyu1GKyGbDe8+2any7N2L78BBJL9/cAelkvjrrsNy2aXoZ8xAk5o6eZONcdYiSRJ9nZ5IeF5rTS9+TwiApCwz2SVWsousZBYmoNVH2Wdn/wuw8T75RuOqh2DWZyZ7RqdMUAxS3lbOa3WvsaVpCz7BR7Y5m2sLrmVVwSry4vNO+9hSKITv4EHcFdtxvvsuvgMHItv0s2cTv3YN5otXoM0+c7fmqY/q+PnrVTxy2zxWzZ7YIhNOX5Dv/ns/mw52cNWMdH5742ws+skXFT2+Hqrt1ezvruTjpn0c6avGLXaiUMjXuBrMxOvisPnbIq+Zbp1OmjGN/kA/zoCT/kA//f5+AuLIXMOhGNSGYaIqsjyK6Bom0HRxGNVGFArFCLF/phxsdXDbn8uJM2h4/u4lZMUE1GkRdX2iTiSiIJwM/8/1dP7yl2jz85jy6KMTX7Iz4IHKV2DXX6G5Qi5JWrJGFlR5F8XcqRjnDSfKifK5XfS2tWJrrKdz21a6q6twuPrx6DRIQz4jJmviEOdqcLQkJo+f2zzGSMEgHb/8FX3PPsto2fXqlBT0M2YMe2jSzn1hFQ0hW2cbbodfFk1VPbQc7sXVK4tzS6Ke7BIrU4oTySqyYoybwIq1p4KvH954QC7QNKUMrv8zWKOrrPbp4A662dy0mddqX6OivQIJiVnJs1hVsIqr8q8iUX/isGNJEPBVVuLZvh13RQXenbuGFLVJRLDLPa5QqUi55x6S775rzOYeEkSue+wT2h0+Nt9/MfHGiRExNZ1Ovvr3XTT2ePj+VcXcuSx/wp0wSZJoc7dRba+mqqeK6h557PJ0RfbJNGVSnFhMobUI0ZdJTVMcH1YHcVGLMecpUIRAUvOjC/7AzbOXjXgPv+Cn3z9EWIUfzoBzxPqB5ci2oHPE8YaiUqgwqA24g24kJBQomJ08m3RzOiaNCaPaiFFjjIwnWmfSmNAoNRxq6+f2pyow69Q8d9fis65h8VgLyjPhrBNRA7jLy2m991tIQPZDv8e0dOm4zuu4dFaG3alnweeApGkw/w6YexuYTq9cZ4wYZzuSJOHds1cO19u0CdHpRJ2RQfyaNZhXrcJn0kccK3lspqetBb/bHTmGWqcjMWO4sErMzCYhIxONNvrykDx79tD0xS8hBYMoNBqyH3sMpVaD79AhvIcO4TtUKZdYD3+HnqvCyusK0FHr4OiuLmp2dIIkl7+ed0UOU+elkJhpQq05tZCkcxm/N0RbuBhEc3Uvve3yZ0Bv0pBVZGVKiZXsYitxyYboL2rSshNe/DL0NcHy78LyB0AVZQ7ZGNDp7mRT/SY21m3kcO9h1Ao1F2ZdyKqCVayYsgK9Wo8kivirq3FXbMdTUYFn505ElwuQG94aFy3EVFaGcdEiAk1Nw747cv7vL2MeDnyozcGaR7bymfnZ/Pozs8f02KOxYW8r33/xACadmkdum8figvGP1gmJIRocDRGxNPDoD8itPJQKJflx+RQnFVOSWEJxYjHFicWjFhAJCiI/eOkAL1ZuRW2sI+Qp4OppZTxy2/wx/RwKooAr6BoursIO18ByeXs5B2yDTmWqMRWj2ogn5MET9OAJeRBHa+Q8CiqFCiGkRYGO7PgEEvSWQdEVFl7DhNgx65qdzVTaK5lunU5efB4hMTT4kELDn4+27iT2ESSBoBgcsS4khuj399PkbEJCQqvS8vQVT0+qkDprRRRAoLmZlq9/A39tLWnf+y7Wz39+8k4yQS9UbpDdqaZtck+JktVhd2oZnCV31GPEOBOCra04Xn0VxysbCDQ2ojAYiLvicuLXrcNYVnZCZ0mSJDyOviHCalBk9du6Bh0ehYL4lFSsmYO5V6Io4O7rpWDegjGpEni6eN5aj+f9TRhXXI3xyttGbBfdbnzV1fgOHYqIq0BdPYjyCVCVkoyhNCyqZsqjOjU1ai+eJVGit9NDR52D9loHHbWOSEU4hWJUUw6FUkFCmpHkbDPJ2WaSss0kZ5kxxmuj9uccKzrqHDRX9aA3afD0B2iu6qGr0YkkSqg1SjILE8gqlt2m5Gyz3HvpbEAU4OOHYMt/Q1ym7D7lLpnsWU0INb01bKzbyBtHN6Jr6mJ+i4ZlnVaya/tROGVBrMnNwbSoDGNZGcZFC0cN7x2t191Y88tNVTzxQR3PfmUxS6aOj6gJhER+8Xolz2xrZGGelUdum0/aOJTL9oV81PTWRJylans1R/qO4Bdk51an0lGYUDhMMBVaCzGoTz5sbVdjL7c/VU4gJBKu9M+CXCs/WTuDGZkT56zv7drLV97+CkExiEap4c9X/HmYcJAkCZ/giwiqgdEddA9bburt5dmdR1GrAlxcHIdCFRjcPxjeP+TBG/LiDXnH7edRK9SolfJDpVQNe65RalApVJHnwx4KNW2uNur75bL+SpR8c/43uXPWneM210/jrBZRAILLTdv3v4fr3c3EX3cd6T95EOVENuYdja5q2Z3aux58fZBYEHanbpeb8sWIcY7g2bMH98dbkUQB7+49eCoqADAuXEj8unVYrrwSldl0xu8T9PvobW8bIbB621oJBQZzkRRKJWu+80OmXVB2xu95ytRugfU3y406VdqT7jEnejyysDo4IKwODhdWycnoZ5RiGOJYqdPSJkVwBAMC3Y39tNeGRVOdA79bztHRmzSkT40nY2o86VPjEQWR1x/ZHymlfckdJSgUCuytLmwtLuwtLpw9vsix9WbNoKgKP6zppugokHCKiIKIq8+Pq8eH0+7D2eOjs76fxoP2QWGpgPT8wWIQ6fnxqDRn38+KowVeuhsaP4YZ18Gqh8GQMNmzGnckSSJw9Cju7dvxVGzHvX07Yl8fAF0JCg7mQHNhPDkXX81lC29hunXyi+V4AwJXPvwhKqWCTfcuQz+KI3wmoVLtDi/f+Odudjf18eWL8vn+1cVoRql4dzIMnUd+fH7EVRoQTPX99RH3xaK1RFylAcGUH5+PWnnmLuiuxl7K6+yU5SdSZ3Pz603V9HoC3F6Wy7evmE6CcWKuN880hO1wh5Nb/1yOVqXk+bsXk5t04vOyIAp4Q96IsPKEPLxw+AVeOvISEhJKlFxXeB3rpq0bJnIGhJFGqRmxbuD5mZy7Pk1QTjRnvYgCkEQR2yOPYnvsMQxz5pD1v3+MjkTuoA+qXpXdqcatsjtVfK3sTuVfHHOnYpyVSKKI//Bh+l58id716wcv9lNTsd58E/Fr16LNzp6wuXy0/hl2bHwpYnsoVSoWrLqOBWtuwGAe4wpQvn7oqYWeOrDXDVmuBY9t+L4aI2TOh5QiSCkeHM2pn5o3eayw8lUewl9bN/i7TkpCP6MU/YwZEXGlTk8fc2HldvhpPyo7TO11DmxNTkRR/j1b042DoqkgnoQ044j3b9i0g6btDeQsyiPv6oUjju/3BCOiakBY2dvcCEH551SqFFjTTbK4yhp0riY7HygUFHD1+CMCqd/uxTlEMLn7Akji8HOlWqci5BfkJwpYcE0eZasLJmH2Y0jlq/DqN+Uy5tf8Vg5jP0fdREmSCNQ34NlegbuiAs/2HQh2OwDqzAzZaVq0CFPZIoS0JN5vfp+NdRvZ2rqVkBSiyFrEqoJVXFNwDanGybs++fiIjc8+Xc6dF6dz29JE7F47dp+dHm8PlfZKNtZtRJAElAolizMWR3K9FCiGfb4VyMsKhQIFCrr6fWwLN7ddlJ9ETuLg94Ei/G9g/6HHGHj90GN1e7p5t+ldBEkYMf9UQyrFScMFU5Y5a8JuKjm8QR56p4a/lzcSp1fzwJXF3LxwSlT3mzrS6eSWJ8tRqxQ8d9cS8pNP78ZmtAiYWE7UCTgdETVA/1tv0/b976OKiyP7kf/FMGvWGM/uDOg+DLv/Bnv/Cd5esObDBQPuVBQIvhgxjkPk4qGiHPe2cjzbtyOE77hGUCpJufceku++e8LnN6xKoEpFdslMGg/sRWcwsmD19cy/Zg1a/SlUHvI5BoVRT/2JhZIlAxKnQmK+7D7t+TuIIVCoYNpl4O2RnWm/Y/A1+oThoiqlCFJL5GOd4EJAFlaHI6GAvkOH8NfWDgqrxMSwU1WKwmgk1NGJvrgI3dSpSMEQUiiEFApCKIQUEo55HkIIhOhzKrA51NicOmwuPe6gLFaUCpFEjZNEtYNEVQ+J9KAVfeFjhI8z8B6CgBQKIjr65TLNkgQqFQk33ohpyRK0uTloc3JQGkdPZhYFkb4uL/aWIeKq1YW7b9BxNMZph7lWSdlmrGlGlKd51/tY/N5QRBCNGHt8ePuHV+NSKBWYErRYEvVYkvRYEvXEJRkiz81WHbYWFxse2jNuTW4nlIAb3vwPOeIicx7c8DQkTZ3sWY0pkiQRbGqSBVPFdjzbtxPq7gZAnZqKsawMU9kijGVlaLKzj3sR3+Pr4c36N3m97nX22/ajQEFZRhmrClZxWe5lmDRn7tSD7Bz0+nsHRZGvJ7Js9w5/3u2xIzFSoChQIDF4jRevjceitQxbJ0lS5LmEhCRJuPwhnL4gapWCBIMGlXLwOEP3H3geee2x+4R38wv+YRXvlmYu5fOln6c4sZgkQ3RUQq7u6Oe/Nhyior6HWVnx/GTtDObnTGzD6JPhaJeTW56sQKmA5+5aTEHKmVW0jiYBEw2cUyIKwFddTcvXv0HIZiPj5z8jfs2aMZzdGBD0QdVrYXfqY1Cqh7hTK2LuVIyoINjWhntbOe6KcjzlFYS65EpG6owMTIsXY1pchsJspu3b3xnXZOiT5dgqgd2N9Wz91z+o3VmBMT6BsutuYvZlV6PWhKtSjSaU7GGxNEIoZcohuUkF8pg4NTzmg/aYC6ChPeYGQvkkCVyd0F0t30zpqpLH7ir5hsoAuriwsBoQV2GBFZd93O8F0esN51hVDgqro0cjwupEhFQ6+uPycMRNxRGfjyMuHyGcL6D1O4jvryO+v54EdyNx/k6UaiUKtXrYg2Ofa9Qo1BoUKhXBtjYC9fXHfX91Sgra3Fw0ebloc3LR5uaeUGB5XYGIsLK3uLC1uuhpdyOG5HOSSq0kMdNEUpaJ5GxLRGDpTZphVQLT8uPwOoOjiqOB5YA3NOy9VRqlLIgSdcOEkiVJjzlRjzlBd1IC7pyoVti+D/79ZbAfhQvvhZU/BHWUVgo8CYbmIqlTUsKCqQJ3xXZCHR2AHFJrWrQoIpw0ubmn5Xw0OBp4vf51NtZupMXVgl6lZ2XOSlYXrMaoMbKna8+wi9OAEBghhoaJoiHLvb7eYWJlAI1SQ5IhiUR9Ikn6JJIMSZhUCTxX3kuSIYmfrVpMijGZJEMSDY4G7n7n7pN2GhzeIN/+1z7erepk1ewMfn3DbEy6Mw+hixbH49OQJInX9rfzi9cr6ez385kLsvneVcWkWKKj8FFtt4tbniwH4NmvLGZa6gS2BDpPOOdEFECop4fWe7+FZ8cOEr/8JVLvv/+UG9RNCLYjspjau16+Y52QG3anPguWtMmeXYzziJDdjrtcFkzuigqCTU2A7G6YFpdhLJOFkyYnZ9jFw0QkQ58Uo4kXn4O2XVv4eMNGmhs7sBhVLM3zUao/itJ7IqEUFklJU8GaN1IojRWSBG5bWFyFBdbA6B4sv4vGBCnTR7pXCbmgHPm91v3oo9gefUwWUkol8evWEbdmDR6/iq4uka4ugc6OEL22YCQ/JzFNT3qeifT8ODKmxhOXakKh0ZxRifnRqhWqE+IJNDYSaGySxyZ5FGzD/x4nK7AEQaSvwzMkHNCJrcWF1xmM7GOwaHD31iIEW1Cqc9DopyAKw89jWoN6uDg6RigZLJpzvujFpyKKUPE4vPsgGBLh+iegYMUkT+rM8OzYSdOX5P+jQ1ElJmJctChSQU9bUDCmf39JktjXvY+NdRt5s+FNHEOcagUK0oxpuENunIHRy18b1UZZFBmSIsLoeM8tGsuoc9+wt5V7n9vLj1eV8qWL8iPrT9ZpqGrv56v/2EVrr5cfXFPCFy/MG9Pf0dnkeLj9IR7ZcpSnPqpDr1Zx3+XT+dyS3NPOBxsL6sICSpTgubvKmJYaXU2uzxXOSREFct+Wzl/+kt71z2Javoys3/0OVVzcGMxwHAj5B92pho9kd6roashZIlf9y19+UgnqMSaGqBEOZ4DQ349nxw7c5RV4ysvxHzkCgNJsluP6w8JJN70w+i4eJQn8Ttkxctug8RN47+fhMDolJE8Hd3fEUZIkaHQn8HFPIZ1uPYlxGi68sJjChYtRJE8bX6F0unh6houqAaHlbB/cR62H5MIhrpX88DT2se/eX9BpnY1CAeqLLqO7RxkJh9PoVKSFxVL61HjS8+PRGsanDPXJflYEl5tgU+OpCazcHLS5eaMKLE9/AFuLk7o9hzi45WX8ziNDjqBEozOg1unQ6vVojQZ0RgManV5+6AdH7THPB/fRodEPvEaHVm9ArdV+qug8UV+1qMbZCa98DWo3Q9E1sOaRs7bBvCRJ+A4ewvHqq/T9+99I3sEqZOYVK0i5/z50hRP3vRcUgvz4kx+zsW5jZF1hQiEL0hdEBFGSPolEw6CTdCoV5o6HJEl84f92sKOhh7fvW0629eR7Bb24q4UfvnKAeIOGR2+bz4K8E/fIOl+o63bxk9cq+aCmm+lpZh5cM4OlUye+1U29zc0tT24jJEg8d9diCtNiAmq8OGdF1AC9zz1Px89/jjY7m+zHHkNXkP/pL5pMbEflOPNdfwW/3NsAtQ7u2BgTUlGAZ9cuGr/wRQiFUKjVpP/8Z5iXLUOVkBDVjWFFjwfP7j1yXlN5Bb5Dh0AUUej1GOfPx7h4MaYli9GXlMhhWROJJEHAJQsity0sjrrDy/bBZXd3+LkNBP/xj5eQBwUXD7pJiQVgzUfSGDi6Yxtbn/8H9pYm0gqmcdHNnyN3ztj2/RhXvH1gq4mIK6mrGmd7Nz09GuyhHOyhXDqC03EKaRBO1jZq3GSlOshIcZOeHiApWYFSb5QdLq1RLoChNR0zhrefaY+f0RzCU+RUBZYyJ4f2RAtHfU7s/X0olEqkIeGNqXnFZBZNJejzE/R5Cfp9BP1+Aj4vQb+foN9HyOcj4PMhCqFjp3NC1GFBpdHpRgiykN9P06H9SJKEWqPhxh/94uwQUjVvw4avyzcurvwFLPjyWVk8ItjejuO1jTg2bCBQW4tCo8Ewfz7e3buRRHFSQ5InK3ytucfDFQ99yOKCRP7yhYWf+j3oDwn89LVK/lnRxOKCRP731vlRE7oWLUiSxLtVXfx04yGae7ysmp3BD68tISP+zIXvydBod3PzE+UEBJFnv7KYovSYgBpPznkRBeDZuZOWe+5FCgTI+v3/YF6+fEyPPy588Bu558ZAjPOc2+C6xyd1Suc7Ibudhltvi4S6DUOtRp2cLD9SUoY/UlMG1ycno9CMf7d4KRDAu39/xGny7NsHwSCo1RjmzInkNennzDmzlgDHywEKuIeInu4h4sg2RBDZwB3efjxRpDHKTauNyfJoSgFj0pDlZDnf6I3vyBXCVFq449UTXrCLokDVR+/zyQvr6e/uJLtkJhfdegdZRdF9QetzBbG3ubC3urG3uehplSvZBX2DCeIWs4Aq1EefzwooUSBQlvASFyS8AUEPCIHjv8FoqLRhUWUeRXAdT4iF1/e3wge/lh1CpQaufxLyLpKPpdaNyYX4UIHVXVVJVfUB6hx2gkiYfQFy7A5MviC78jMQFQqUksSlOdPJv3Y1+uJi1BkZJ7xwFEIhWWT5fIPjwPJQ8eWTl4eKsmGv8ftw2m34XIPhWSXLVnL1N+6PXgEf9MmhexWPQ+oM+MzTcgGUswjB5cb5zjs4NmyQ2y9IEob584lfu5a4q65EFR8fNZEFkxW+9tRHdfz89Sr+eOs81szJPO5+rX1evv6PXexrcXD3xQU8cEUR6kkMV4t2fEGBJz6o47H3j6JUKPh/l0zjzmX56NTjl1rSZPdwy5Pb8AYFnr1rMcXpURp9dQ5xXogokJuANv+/b+Kvrib1O98m8Utfit6TF8gXp8+skS96JBGQYOV/wrJvx4pPTAKeXbtove9+Qj098gpRRKFWk/S1r6EymQh1d8sPmy2yLAzsewwqq3VQYCUnyyLrWOGVnIzSdPIhZpIg4KusijhNnl275DAVhQL9jBmR8DzjBfOPWxXtlGksh7+tlv+PKpRyLlHQK4ujkG/016gNsvgxJQ2KIFPyEKEU3jaw/mTD7E7D8QgFgxzY/CblLz2Px9FHwfyFXHjz50jNm9yy06GAQG+HB3urXJXO3ubG3urC4xgUQDqTmqRMuey3XEzBTFKmCa1BTce2bWz4Wx+CpEKlEFj7+QTSl4QbnwpBWUwF3BDwQPDYMbwt6Dn19cf7m4+GUgM6M2gt4dE8ZLQMjiPWDX+NqDZQe6CSve++SdOBvShVKqYtWsrcy68mI6eAUHMT9qefpnHLZuxmA0kuL1bPoGBXxcejKylBX1yMvqQYXXEJuoL8cbnRMVBFMhQMRsrxp+TkUXb9LUwvWxpdTnZXNbz4Zeg8CGVfhct+Apqxb5g6HkiCgLu8HMeGDTjfeRfJ60UzZQrxa9cSv2Y12pycyZ5iVCGIEtc9tpW2Pi/v3n/xqH2PPqzp5t7n9hAUJH534xyumpk+CTM9O2nu8fCL16t481AHeUlG/mv1DFYWj31F5uYeD7c8WY47EGL9nYspzYwJqIngvBFRIIc0tf3ghzjffJO41avJ+NlPUeqj+MQwcGGYtVAunXzgX1B0rexI6c/Syk5nGZIk0fOXv9D1+4fQZGeR/fDDiD7fSd25lIJBQnY7oe5BYTWa2ArZbLJLdAxKo3G4m5WSgmqI0xVsa8O1+T0Elwv/4cOITvkut65wWqQQhHHhQlTxY/x/xe+Cfc/C+7+UnaYBEqfJAiYiglKOEUinIIomkKDPx+43X2PHq//G73ZTtHQ5F950O9aMrHF9X1GU6O/2RtylAWfJ0eWJFHxQaZQkZphIyjSRGBZKSVlmjPHaE94E6ti2jda9tWTNnToooMYbURgUWQGX/P312r1y82GlGpbeI7dz8Dvl7X7XkOUh6yKjM3wDaSSuoJYDfens70vHFdJh1gSYk9rPzMwQZotxmNDy1HXT9EwNkihr/Sm//B6KKXPxV1fjq6rGV1WFv6YGyS+LK4VWi66wEF1JMfriEvSlJeimF41J0+iBnKis4hk4ujqoeOUFettaSMyaQtl1N1G8dDnKySyCJEmw8y/w1g9k0bruMZh+5eTN5xTw1dTg2LCB/tc2EurqQhkXR9zVVxO/dg2GefOi+6bpJFPZ1s/qRz7m+nlZ/PbGOZH1oijxyJajPPRuDdNTLTz+2flnXCL7fOXDmm4efO0Qdd1uLitJ5UerSj+14e3J0tLr4eYnynH5Q6z/ShkzMmPXhxPFeSWiQL4otj/xBN0P/wH9zJlkP/K/aNLPgrsqkgQVT8gnt8R8uGW9XKErxrghOBy0ff8/cG3ZguWKK8j4xc9RWcY+vlgSRQSHY6TQGnC0hogw0eMZeQCFAvPKFcRdcy2mskWoU1LGfI4A9DbA9j/D7r/LfY+Sp9PRoabVX0qWvob0u/7nrM7b87lc7Nz4Erve2IAQDDJz5eUsueFWLElnlhgsSRKe/gA94TA82WFy09vuJhRuKosC4lMMEUcpMdNMUpaJ+FQjyihu4vipnElOlCTJzmZYZEm+fpqrDrHvkx0cqaxDkiRyc5KYW5JCQYYWZdA5UoT5XeDqwtMewNOlw5jqx3jRlXDDU6AZzFGQQiECDQ2yqKquwl9Vha+yalg/NE1uDvqS0mGulTo15YwuzkVRoKZ8KxUvPY+tuZGEtAwWrbuR0uUrUanHP+x3GG673Dj38Osw9RJY96eorxIbstlwbNyI49VX8VdWgVqNedky4teuxbxyBUpdLF/nZPnVpmr+9EEt6+8sY+m0ZPo8Ae57fi9bDnezbm4m/339LIzaCc6XPccIhET++kk9f3j3CEFR4qvLC/jaimkYtKd/46S1z8vNT2yj3xtk/VcWMzMrJqAmkqgTUSXTZklb3v543HtoON97j7bvPIDCZCT7j388e6qsNWyFF+6QLy7WPQ6lUdYH6xzBe+AArd+6j2BXF2kPPID1c5+NijuZottNqLsb+1/+Qt8L/440Mk255x6S775r7N9QkqDhY6j4Exx+A1BA6VpY/DU6AtN55fe7EQQJhUJB4aJ0rGkGNDo1aq0SjU6FWqtCo1MNWVZG1qm1qqgUCO6+Xipe/hf73tmEQqlg7sdh2vAAACAASURBVBXXsmjdjRjjRn4niYKIEJIQgiKhoEhHnYOWwz1o9WpCQVF2l1rd+NyDTqMxTktS1nBnyZphQnMGJ9JzGZ/bReUHm9n3ziZ62lrQmy3MXHk5sy+7Cmv68XM4IjRvh2dWQyiAnGMqyc7owjvlh3n0mw6SJBHq6pKdqqqqsMCqHpYTqUpKGiaq9KUlaHNzT7mlhiSKHN1VQcVLz9NZdxRLcgqL1nyGmSsvR30mOYsnS90H8PLdcr7i5T+Bsq9Fbdi46PPheu89+jZswP3xVhAE9DNnynlO116DOjFWKe508AUFrnz4Q/xBgUtL0ni7soM+T5Afryrls4tPry9WjNHp7PfxyzeqeGVvG1kJBv7z2hKumpl+yr/jtj4vtzxZTq8nwPo7FzMrOyagJpqoE1E5KUXS9z/zOLNWZjPnkinEJY9fRRP/kSM0f+P/EWpvJ/3BB0m44fpxe68xxdEK//o8tO6Ei+6HS/5z1H4xMU4dSZLoXb+erl/9GlVKMtkPPYRhzpxPf+EEc2wPnjGvKhX0wYEXZPez84DcF+aCL8gXnfFZiILIa4/so6VqsFmsQhFJ9ThpVBolGq0KtU4ehwovtVaFRq8Krx8uvjQ61bDX9XV7sTU7SZ5iwZpuRAiKCCExIm7EkDwOCB4hJD9CQ/Y7dvS67PQ0b8Hdsw+FUoMhYRE680JEURPeT0ISj/8DqzRKkrOPyVvKMmEwn72NSSeSzrqj7H37Daq3fkAo4CdjWhFzrriG6UsuQqM9RYdhwBHLvUjO4dv2CNS8CSodzLkZFn8DUotP6lCCyzUYClhdhb+qGv+RI5FeQwq9Hl3RdDkUsKREFljTp6M0GD61iIEkSTTs3cW2l56jvaYakzWRhauvZ/ZlV6HRjUPoeSgAW34BW/8ASdPk4hEZ0fd9J4ki3l276NuwAeebbyG6XKjT04lfs4b4tWvQTZ062VM8J/jr1noefK0y8vyX18/i1kWxHLLxYnt9Dz/ecJDqDicXTUvmwTWlJ93PqcPh4+Ynt9HjCvCPO8uYMyVhnGcbYzSiUkR974bBKnRJWWby5ySTPyeZlJzRm8adCUJfHy333YdnWznWz3+OtO9+d+JLPJ8OIT9s+q5cCn3qJXDD02CM3YE7EwSXm44f/4j+NzZhung5mb/6FWqrdbKndVzGpapUfzvseAp2/Z+c75RaKieWz74pEv5kb3Wx+ZkqupuckQJrKrWStffNIyXHQtAvEAoI4VEk6A8R9IvD1gcDAiG/QDAghkchMg6+bvj+YmiMv3sUoFYrUWmUqMKjWqNEqZbHgXUhfzf2xs30dx9EpTGSWXQZGdMvRKPXD9u/pbqX+n1yyW2FAhatLmDBNXljO+dznGDAz+FPPmLfO2/QcbQGtVZHyUUXM+fya0grmDa2b2Y7AtselfP7Qj6Ydjks+YbcQPYUzzNSIIC/vj7sWsl5Vr7qasT+cJsKpRJ1Rgah9naQJBQaDVOe+BOm4+SrSZJE86H9lL/4HM2VBzDExXPBteuYd+W1aA1jVBjGXisXj2jbI98gufK/oy5n0V9fj+PVV+l/9TWCra0ojUYsV1xB/Lq1GBctiq5iHOcAj245yu/eOowEqBRw/xVFfGPlGH/uYgwjJIis397E7946jCcg8MUL87jn0kIs+uOH83b2+7jlyXJsTj9/+/Ii5uVE73XKuU7Uiajc1CLpBzc/wSV3lODq9VO/r5uOWgeSBKYEHXmzZUGVPd2KSjM2X6BSKETnb35D79/+jmnpErJ+/3tUCWeJqt/1jFze2ZION/8TMmZP9ozOSnyHD9N677cINDWR8q1vkXTnl8+vE3TLLrmU8aGX5QIBRVfL4il/eeSCUhREdr/dxI7X69Hq1Vx8axFmq47Wml6yplvHPQRXFMRB0TVEeFVubaO6vEOO1FJAcVk6RYvTUWlUYZGjGCaKBkalUnFKN2U6ao/w8XN/o3H/HsyJSSy54VZmrLgMVfimS0edgw0P7UEQRFQqWVSO9+/kXKG3o41972zi0JZ38LldJGZmM+eKayhdfgl60zgnsrvtsPNp2P6kXG4/baYspmbeIJdiP00kSSLU1oYv7Fr1b9pEoLZ2cAeFAt20qehnzkI/ayaGWbPRF01HcUz4Xkv1ISpe/hcNe3ehN5mZf81a5l21Gr35NH8vQlBun/HJ/8o/37rH5BDdKEHo66N/0yYcr2zAu28fKJWYliwhft1aLJdeOnbVRWOMYFdjL7c/VU4wJKJRK/nnnYu5IDd2gT4R2F1+fvf2YZ7b0UyyWccPrilm3dysEeeorrCA6uz38bcvl8X+PpNM1Imo0umzpffe/GjYxYfXFaDxgJ36fTaaKu2EAiIanYqcGYnkz0khd2YSetOZJ+H2vfgSHQ8+iNJqxXLZZcRfczXGBSf9u5k8WnbC858Dby+s/oMcnhLjpOl78SU6fvpTVHFxZP7P7zAtOnuLI5wSQhAqN8j5Ti07QBcH8z4Li74iN6gdwlD3adoFqSy/ZToGS3SEpU20eGk+tJ+Pnvsb7TXVJKRnsPSmz1K8ZBkKpZKOOseEicqzHVEQqN29nX1vv0Hj/j1yefKFS5hz+TVMmTFr4nMwBkJYtz0K3VVgTpc/Cwu+NCYufyQENxBAoVIRt2YNIVs3vgMHI+0QFBoNuuJiDLNmop85C8OsmWgLClCoVHQcraH85X9Ru7McrcHIvKtWMf+ataPm6o3A2QlH34Gat+Dou3IVRQC1Hu54bdILwkiBAK4PP5TLkr//AQSD6AoLiV+3jrhVq9CkjX1J6Bijs6uxl/I6O4sLkmIX6JPAvuY+fvzqIfY197Eg18pP1s6IVNvrcoYFlMPHM19axIK8WPTRZBN1IurTqvOFgoIcNrPfRsM+G57+AAqlgsxp8eTPSSF/TvIZ5VH1Pv88Hf/1YOS5KjkJbU4umowMNJkZqNPT0WRkosnMQJOejjI+PjoSLl1d8MIXoHGr7CBc8XNQTXB1p7MM0eul46c/w/HyyxgXLybrd79FnXxmldjOCtx2OVxvx1PgbJcFU9lXYe5tcj+eIQiCyJ63ZPdJZ1Sz/JYipl0QfRc0Ey1eJEmibvd2Pn7u79iaGkjJyePCWz6P3myhpfIAU2bMInP62dWYdKJw9fZw4L232L/5LVx2G+bEJGZfdhWzLrkSszUKLgokCWo3y2Kq9j25gfDc22Dx1yHpzHJvRgvBHXCsvAcO4jt4IDweRHS7AbnVgb60FP2sWRhmz8KZZGX31i3UVHyCWqtlzuXXsHD19ZgShlzwioIcplfzFhx5G9r3yustGXLUQts+QASFCi75odx/cIJxb9tG/5tvIfT04Nm+HcHhQJWcTPy11xK/bi264uLoOLfGOK8ZaEswkd/poijx790t/HpTNb2eALeX5bIwz8qDr1Xi9of4x51lLIwJqKjgrBNRQ5FEic7Gfhr22ajfb6OnTT7pJGaa5Dyq2Smk5lpQnEIFMNsTT9L9hz+AKMrhFsXFqCwWgu3tBDs6RvTzURiNaNLTjyuy1BkZE1dmVQjCOz+G8scg90K48a9yX5YYI/DX1dN67734jx4l+WtfJfkb3zjlilpnHZ2HoPxx+W57yAcFK2Hx1+RckFFCF6PZfYoWJFGkettHfPL8P+jrbI+EPqrUaq695wHy5l5w6kUQzkHkHJ8D7HvnDY7u2IYoCOTOnsecy69m6gVlk9sb6UR0HoJtj8k9+oQgFF0jh/rlLj3lvKlTQRJFAg0NePfvx3fgIN6DB/BXVSMF5IbLqoQEAqXFHDGqqbd1oFSpmHXxChaWWonr+kR2nTx2uTFW9iIovFzu95Q2U3adBxq5q7Rwx6tj6kRJgiD3x+vsItTVSbCzU17u7Aw/7yLY1iY3Aw9jXLKEpC9+AdPSpWdHTnKM84LGA3t5+Vc/QRBCqNRqrvr6feTMmI3WaEI9Dg25j8XhDfLQOzX8bVsDAzWMtColz94VC7OMFs5qEXUsjm4P9fts1O+z0X60D0kCY7yW/NnJ5M1OJrvYilpz4pP1iaqeSaKIYLfLgqqtnWBHO6HIcgfB9nYEm23EMVVJSbLQysxAnZGBJl0WXJqMDNTpGahTksc2/2b/C3K/D0MC3PR3mLJw7I59DtD/xhu0/+ePUOh0ZP7mN5iXXTTZUxo/REGuQlb+uFydTG2AObfIztNxqpHJ7lMjO15viGr3KZoQQiE2Pvwrju4oH7FNazBgjEvAEB+PMS4B47AxHmN8QuShN5tRngOVNkVRIOjz0XRgHwfff5fupgacti70JjMzVlzGnMuvHvdmxmOKsxN2/Bl2PA3eHsicB0v+n5xLNEGOvxQI4DtyJCKqfAcO4j9Sg1utojbVSmuiBQUSU5X9LJyRRurl16Gafc3ooYin2b9LdLsJhsVRqFMWREPFUaizU24eLgjDX6hShZuGp6JJSyXY1Y1v//7xb9EQI8Yp4LTbaD1cSWt1JW2Hq+hqqENOth2JWqNFZzKhM5rk0WSWl43GyLI+st08uF94VGtO3DB9KA++eoi/ftIAxAp+ALRUHeLI9k8oLFtKdvGMSZ3LOSWihuJzBWk8KDtUTYd6CPoF1DoVOaWJ5M9JJm9mMnrz6Ce/M6l6JgYChDo6ZGHV3k6ooz2yHOxoJ9TWPrJhqkaDJi1Ndq4yM2QnKyMd0eMl1NOD5dJLTr36WscBeO52OVzr6t/Agi+e2uvPQcRAgK5f/Yre9c9imDePrId+f3Y0Wj4dfA7Y8w85Wb63AeKy5fyO+Z8/YX6HrcXF5mcqsTW7KFyQyrJbpsdKcp8kbTVVvPCzHyIEgyhUKhZcex1agwFPvwOPow9PvwOvow+3ow9vfz+SJI44hkKhxBAXFxZX8RhGiK7ho0ZvGPVEfKohKJIkEQoGCHq9BLxeAj4vAa9HXvZ6ws8HtwVH7OMl4PNEtof8/mN/MMrW3UjZ9Tef3c5cwCNX8yt/DOxH5c9V2d1wwR2gn4AcOL8L6j+QQ/SOvIPY04avV4M3mIvdncwBp0iTTo2kUJDZ66JYYyRlxqxIjpW+tASlXj/iHCcJAiGbfYg4GnSPgl2DywMhhkNRWiwRcaROTUOdloY6LRVNWlr4eSrqpKRhTv+4t2iIEeNTEEUBW1MjbYerZOF0uBKnrRsAjU5PRmERluRUqj/egigIKFUqFq29EWN8An6PG5/bhd/jxu92h0cXfo9H3uZyIQqhE76/Sq0eKa6GLOuHbGt2STz6+k5M/n5aLPk8dM9154UTJUkSrh47tuZGbE0N2JoaaDtymL6ONgBUGg03/fi/JzV0/pwVUUMJBQVaD/eF86i6cTsCKBSQMS0hUj49PmViKv5IkoTY3y87V21tstBql10sebmdYGfn8Dt5CgXx119P4u23oSspOfk4cU8PvHinHN8///Nw9W9BMw69Rs4CAi0ttN77LXyHDpH4xS+Sev99KCbAjp9wbEdh+xOwdz0EXJCzRHadileB6vhhMoIgsvvNRna+IbtPF99WxNR5MffpVDlZ8SKJIl6XE+8QgTVsdDhk0dXfh7uvj4DXM+px1FrdMEfLEBePKAgc/uRDRFFEqVRSdOHF6AxGgr7RBNKg+JHEkaJu1PfU6dDqDWgNBrR6ozwaDGgG1hmMaPUGOo7WUL9vl1zSW6nkwps+S9l1N53W7zXqEEU48pacN9XwEWjN8nds2VfBmju272WvlUVTzVtyzqsQAK0Fpq6Awith2mUQlxHZ3dHYQMX6Z6jcvwtBFMn0Bilo6iDOFwCVCk12NsHm5kjIuiohAaG///juUVoqmuOII01qKkrT6ZVFH5cWDTFiHIegz0f70cO0HpZdpraa6sj3qtmaSGZRKVnFpWQVlZKSmx8JMT6dnKiBm1J+94DICgussPDyDRVebjd+r2dwOSzMQsHAcY9vTc8iNb8Aa2YW1owsrBmZWNOzTr9iZxTg97jpbmrA3txId5MsmuzNjfjcrsg+Zmsiap2Ovo52gKg4r5wXImookijR1eSkYb+N+n3d2FvlO2vWjIE8qmQkUaLtaN+kVdiSBIGuhx+m5+m/yCe6IagzM7BccqnsTi1Y8OlCQBTkcrYf/Q6yLoCb/gbx2eM4++jD+d57tH3/P0CSyPzVL7FceulkT2lskSQ5Ab7iT/LFlkorl2cu+ypkzv3Ul9tanGx+pkp2nxamsezmwpj7FGWEAoGwqDqO6IqIrz7cvb3DXC6FQoHOaEJjMAyKn7DQGRA/OqNxUASdSCDpDSedvxRx5kJyPsGNP/rFuVlso22vLKYOvQSSCCVrYOk3Ifs0K7uG/LJYqnlb/jz3hMuiJ0+HwivkR84SUJ/4M+px9LHr9VfY89brBH1e8vKmUmpJQvPRJwQbGyP76UpLMS9fJoujsEDSpKWiSkw89/NEY5yzuHp7aAuH5rUerqKroVa+UaRQkDwll6yiElk4FZUSl5IadUVMQoGALKg8HnZv2sC+dzZFutlbMzKRRAlHV+ew73qDJS4sqsLCKiyyEtIzoiYCIBQM0tPaLIuliMPUiNPeHdlHazCQPCWP5JxckqfkkpyTR/KUXAyWuKg7r5x3IupY+m1eOY9qv422I31I4uDPp1QrWPuteWROm/ieUceGO2T94WEEmw3n5vdwb92K5PejjIvDfPHFWC69BNNFy1CZT3BHsOo1ePlrcl+Qm56BvHM4DyiMFAzS9dDD9PzlL+hnzCDr4YfQTpky2dMaO+o+kIVTx0FwNIEpFRZ+WS7JfBIFRSLu0+sN6MwaVtxaRMG8lAmYeIzxpPVwFf/+2Q8jydCf+c9fkFU0OSeZyahsNWk4WmUXeOdfwe+AKWVy3lTxtfBpeW6O1kiIHnXvQ9Atlx/PWxYWTpdDYv5pTcvrcrJn06vs3vQqfrebnKnTifu4goACkv0h5jz+p5gTFOOsRhJF7C1NtIZD89oOV+Lo6gRktz59WiFZRTPIKiohY3rx+PeeG2OOJxyEUJC+zg5629vobW8d8mjD3dsz7BiW5JSIwErMyIwsx6WkjkthH0kUcXR3DQvFszU30tveihh2vZUqNYlZ2bJQCoullJw8LMkpJxS10XReOe9F1FB87iDvrz9M7a6uyDqNTkXphZkULkwjNc8yoXcrjhfuIHo8uD/5BOfm93Bt2YLQ14dCo8G4ZDGWSy7FfMlKNKmjXER318Bzt0FPnVwCffHXxrXC1GQS7Oig9b778e7Zg/W2W0n93vcmrkrieCGK0HkQGj6WRXHTJ+ENClj+ACz/zkk3Bz3WfVp+8/Tj5gjGOPuIppPMeYffJecjlj8GfY1gzZPLo8+9Hboq5fC/nKXyvkfCblPnQfl5/BRZNE2/UhZQ2rELM/d7POx9ayPbX/03gXBerkKh4IJV1zFz5eUkZmZH3d34GDFGIxjw03G0JpLP1FZThT+cr2eMTyCrqJTMohKyiktJzStApT77z22n+p0e8Hro7WgfJqx621vpbWvF7xnMbVSq1MSnpcvOVUYWiQMuVkYWJmviSX0nePodEZE04CzZWpoI+gYrcMalpA1zllKm5GLNzDrr/zYxEXUMQxt3KhQK0vLj6GzoRwxJxKUYKFyQSuHCNJIyo+NOhhQK4d2zB+fm93Bu3izHugP6ObMjYX/aqVMHPwi+fnjla1C9EWbdCKv/OKYn6mjA9fFW2h54ANHvJ+OnPyV+1bWTPaXTQxSh65AsmgYevj55m8EqN1eGU+r1IoREdr3ZyK43wu7TbUUUzI25TzFijDmiANWvw7ZHoLkCtCY5VE8cknCuVMuheYWXy+IppXjcb2x98sJ6tr34bCQ0aABjfALZJTOZUjqL7NKZJGXnxERVjEljqGhISMsYVjWvs742UrghMWtKJJcps6iEhLSM2P/bEyBJEl5nP71trSMFVkcbwpA2PhqdfjA0MCys/B4PzYf2o1Sr8Dqd2Joa8Dj6Iq/RW+JIGRKCl5yTS1J2LjrjuXWdOUBMRI3CsY07/Z4gtXu6Obqzk5bqXiQJkrJMFC5Mo3BB2hk19x1LJEnCf+QIrvfew/nuZnwH5bub2txczJfKgsowd678BfPx7+G9n0PaDLj5H6cdKhJNSIKA7dFHsT3+J3TTppH1h4fRFRRM9rROnhOJJmu+HIKZv1zuAdbfesq9XrqbZffJ3uJi+qI0lt0Uc59ixJgQmnfAa/fIThQACjlvcdXvJ6aq3xCODQ266uv34fd4aKk6SHPlAVx2uU2HIS6e7JIZYVE1i+TsnLFtxREjxigEA36qPn6fzU89PqLCnUqjIX1qYSSXKXN6MQZL3CTN9NxDEkWcdhs9YXHVFwkTbBuRfwVgzcwis7BkmMNkSrCeVyI2JqJOEU9/gKO7ujiyo5OOOgcAaflxFC5IY9qCVEzx0RMyFuzslAXV5vdwV1RAMIgqMRHzyhVYLr0UU6oP5cavyTvf8DQUXja5Ez4DQjYbrd95AE95OfHr1pH+Xz9GaYgOcXtcRDEc3vOxHOLTuHXQXRoQTXnLIO/C0YuBnGSvFyEksmtTA7s2NaI3a7g45j7FiDHxNG+HZ1bLTXvHocntqXC80CBJkpPVmyv301Ipi6qBss96SxzZxTOYUjqT7NJZpOTkxURVjDPC53LR1VBHV0OtPNbX0tPWMqJSaN6cC1h8wy2kFUybkCa3MUYSCgb5aP1f2b3p1XOz4uppEhNRZ0C/zcvRXV3U7OjE3uJCoYDM6VamL0yjYF4KelP0fNgFpxP3Rx/JeVQffIDocqHQ6zEtmodFtx+zuRb11T+Ei+6Hs+zE6Nmxg9b7v43Q30/6j39Ewg03TPaURueEoikvLJqWH180nQbD3KeysPsURf8vY8Q4rzjNJreTiaOrU3apDh2gpepAJGFfbzKTVTJTFlUlM0nJyz8nGkXHGHsG+v1EBFN9HV0NdfR3d0b2MVsTSc2fSmpeAWqtjm0vPosoCFFRgS2GTLRVxosGYiJqjOhpc3NkZydHdnTi6PaiVCnImZFE4cJU8menoNFFz8lFCgRw79iBa/N7ON97j1BHByjAkOzHMq8Ayzf/iLawdLKn+alIooj9qafpfvhhtFOmkPXHP6AvKprsaQ0iitBdNSiaGraCN1wxJyKalsnheQljWzVQCIns3NTA7rD7tOL2IvLnxNynGDFinBn9tq6IS9VSeZC+Trlni85oIqu4NBL+l5pXMC5Vv2JEN5Io0tvRRld92F0KP7z9cuQOCgXW9ExS8wpk0ZSbT2r+VIzxw6sgx4rjRCexv8twYiJqjJEkie4mJzU7Ojm6swt3nx+1Vkn+7GQKF6aRU5qEShM9To8kSfgOVeLavBnnxn/hb7YDoMvPwXzl1VguvRQpGMSzY2dUNUUM9fbS/v3/wPXBB1iuuoqMn/8M1WQ3mjuRaErIlQVT/viIpqF0N4Xdp1YXRYvTuejGwpj7FCNGjHHBabfRUnmA5qqDtFQeoLe9DQCtwUhWcalcrGLGLNLyp8VE1TlGKBjE3tw4zGHqbqwn6PcBcuW35Cm5pOYXyKIpbyopuXloDedmkYEY5x8xETWODDTtPbKjk9rd3fjcQXRGNQXzUihcmEbWdCtKZXQl4AW2vYzrT9/F2QCebg0M6ZuFUonlssvQFuSjslhQmi2oLGaUlrjwOLhOYTCMW3Khd98+Wu67j1C3jbTvfQ/r7bdNbCLjQEhOzoWgjxsenueRRWhENOVdJIfnJeSM+7SEkMjONxrY9WYjBouGFbcXkz87edzfN0aMGDEGcPXYI4KqufIgvW0tAGj0hkFRVToLQQjRVl0Zu6N9luD3eOhuDDtL9bJosrc0R4o/aPQGUvPySc2bGnGZkrKnnPUlrGPEOBExETVBCIJIc2UPR3Z2Ur/XRtAvYIzTMu0CuWR6Wn5c9FQ0cbTCvz5HqG43HQ2LcO5pjmxS6HRIwaDsupwItRqVOSysLGZUZgtKi0VeFxcWXebwtuMIMoVON+x34tm9B/uTT+L66CM0aWlk/eFhDLNmjddvYThCSO75cngTvPsgiMHh2xNywqJp2YSJpqHI7lMl9lZ3zH2KESNG1ODu6x2SU3UQe0vTsO0KpZILrr2OgvkLSMzMxhifED3nwvOQtpoqandtx2CJRwgGIi5TX0d7ZB9jfELYWSqI5DElpGXECo3EOO+IiahJIBgQaDxg58iOThoP2hFCIpYkPYUL0uQeVFmmyT+JBH2w6QE8bz9L0/spSCIoNBpy/voMhrlzEd0eRJcT0elEcLoQXU6Efqc8Op2IA+ucLsT+fgSXS97XNbDNNaJPyQg0mogQU6hUBBoa5NcolUx54k+Yly0b259ZCEJfk9yMuKcO7LXh5Vp5vRg65gUKKF0Dl/8MrLljO5eTnrKc+7TrzUaMYfcpL+Y+xYgRI0rxOPrY8syfqd76wajbdSYTiZnZJGZOITErW17OyiY+NR2VWj3Bsz0/8PQ7aD60n8qPtlC3a/uwbfFp6ZFQPDksbypma+IkzTRGjOgiJqImGb83RP3ebo7s6KS5uhdJlLBmmJi+MJWENCOObm+kX9Wk8M5/4dnwKJ4uHcZUP8ZsM1hzwJIJcRnyaEkfXI7LAH3CpzaMlEQR0e0eLsKcA6JsqAiTl72HDhFsaJBfrFKRcs89JN9916n/PEOF0lCR1FM3UihpLZBUAIkFkDhVHgU/vPkfUVGmuGprG9teqcXrDFK8JJ0LPxNzn2LEiBH9HFvl65pvPoBGr6entYWethZ625rpaW3B1dsTeY1SpSIhLWOIsJpCYmY21sws9KZJzoc9ywgG/LRWV9K4fw9NB/bR1VALyH2YBpqtKhQKyq6/mQtv+uxkTjVGjKjmVEVU7DbQGKMzqClekkHxkgw8/QFqd3dxZGcnFa/WR/ZRKGHWimymzkshOduC1jCBfwZ9HMYUAWOyC1BAYh4Yk8HRDC3bB3OAhqI2DBdVlgyIy5RHSwbEZaAwp6OyWFBZLJzMZb9nzx6avvglG6xG7wAAEQpJREFUpGAQhUaDcdHC4+98IqHU2wiSMLjvgFDKmAMzrpeFUtJUWTSZkkcXg2kzJ7RMsSCI9HV4sLe6sLe6sLW46Wpw4HPLgk+pVjBjWVZMQMWIEeOsIHN6CTf+6BcjqnzlzR5euMjv8dDbJgurnraWiMiq271zWCNWY3zCoLga4mDFJafEQswAURToqq+TRdPBvbQerkIIBlGq1GQVlXDhzZ8jd9ZchFCIF//7RxFxmz/3gsmeeowY5xQxJ2qC+OSlo+x5u2nUbfEpBlJyLKTkWEieYiZligWDRTs+E2neDs+sASEwuvMS9IGrA/rbwdkWHtuhv00ene3yOsE/8timlOECKzJmDAougzUiZDxvrcfz/iaMK67GeNmNowslezj0bjShNOAmJU0ddJeOJ5QmCU9/AHuLC1tEMLno7XAjhuTPnVKtIDHDBPz/9u49OM7qvOP499FqdV1ZkmVJtmzLYAgJBRubi4FJaJwMUJoLMKQtbdNAyaQhDCQttDMlkwKlTTKhTWg7mcJAm87QNqFNmGZcUlJCCQZKCOFug4HGkBjZWJJlW9JKsqzb0z/eV9JqdYk2eN+zNr/PzM5K9q7Pz7uro/O855z3hZ6OASAqss++eC1nXHRcqNgiIomZGB+nr7tzRmF1YM9uDuzpYHhwYOpx5RWVNK5om1oSODmD1biijXRlFXBsnrLZ3enr6mTX9ufZtf0FOl7aNvW6NK85nvZ1G1izbgOr3nMK6aqqGc89Fl8PkWIpynI+M2sF7nP388wsDfwHsBT4hrv/00LPVREV6Xyjjy1/8zzj4xOkUmVc+AenYGb0dGTZ1zHAvjezZPcPTz0+01jJstV1U8VV8+oMtQ2VR2Zv1du9QKR7dFHZycJq1n1cbA31zH5ueXW0XLAyQ2fHGHsOn8zKipdZXvFTIOfkFkdRoQTRXqYDnYPR7NLu6YLpUHb6hBW1DZU0rcywbFUtTSszNK3K0NBaQypVNuvzccn1G8Mt+RQRKQHuzqFsPwf2dMwssN7aHV0kOGf8sqS5hZr6Brre2Im7k0ql+PVrb+DETecelWeUm9zXtGvb8+za/uLUhWzrmppZs34D7es20H7KemobGgMnFTl2HPEiyswagXuBFnc/3cxuAJa4+5+b2QPA5e6ene/5KqKmdb7Rx57/OzjvnqjhwdGoqHpzgH0dWXo6shzsGoL4LaquS0eF1erpWav6ZdVYiZ1WfcrYYUZ63mJo714Gu3oY3N/HwIEhhvrH6Nlfxp6hdwGGMcHxS3fSfEIbVU1LqW5dQfWyJqrrKqiuq6Cypjz8iTli7s5g70jOUrzovrdziIn49PGpdBlNbXGhFBdLTStrqc4sPLv4iz4fIiISGRsZ4WDnW3FhFe256tixncGcfVcAZmVkmppoaFnOkpZWGlqWU9+6nPqW5dS3tJbM2QPn29dUWVPL6lPWsWbdRtrXbaBxRVtJ5BU5FhWjiFoCGLDF3Teb2X8CN7r7DjO7EXjK3R/Je86ngU8DtLe3n7Fr165C/x8SGxkeY/+eQfa9mZ0qrA7sGZwasFdUpXIKqwzL2utobK2hLFXcdePjoxMM9h1msG+Ewd7D0a0vvvWOMNR3mIHew4wOj896bnllivKycYYPOdFHy0mljPHZDwWgrMyoyqTjoipNdc7XVZkKauoqqKpLR/eZ9BErusZGxjmwd3CqUIpmmQYZHpyeXcosrWTZqjqaVtbGs0wZ6ltqSu56YSIix7rcE1yUlaU48+LLKCsro6+rk97uLvq6O2cVWeWVldOFVXPrVIHV0LqcJc0tU8sEj7SF9jW1vfs9rFm3kTXrNtC6Vhc1FklK0c7OZ2Zb4yLqYeAyd++Li6V+d/+3+Z6nmagjb3x0ggN748IqLq727x5gbDRaDpdKl7FsVbS3atnqDM3tdTS1ZUilo8JqoRmPiQnnUP9IXAzNUST1Rt/nFhKTysqN2vrK6NZQEd/Ht/qK+L6SdFWKrp/1s+X2ZxkfjwqoS244g+bVdRwaGOXQwAiHsiMcyo4yPDDKUHaE4ewIQ9lRhgeiPz+UHWFkjgIN4qKrLk11Ji666ipmFF65f16VSXNw7yBvbOuhOpNmYsynCqberqGp1SLllalodmlVhmWTM0wra6msOfqWiYiIHKt+0R6g0ZHD9Hd309fdSW9XJ33dk7cu+ro6GT08POPxtQ2N0axVTnE1WWxlGpcu+kQXC+5raj+O9vUb593XJCLJSKKI2gJc7e6d8dK+Tnf/1nzPUxGVjInxCQ52DdET76+KlgVmpwqNsjKjsa2WTGMlHTsOMDHulJUZa9Y34RMwFBdNQ/0jsy73ZAY1S6IiqKY+ryhqmC6aqmrTBc0Avd3la+OjE3HBNTp9nx2JCrHszO+HFyi68i1ZVjU1qxQtxSvxZZMiIvK2uTuH+vtyiqto9iqayeoku79nxj6sVHk5S+JlgfUty2lomZ7JGurvZ/eObZSnK+jv6Z61r6l93WmsWb9R+5pESkgSRdTNwA53v8/M7gHucvcfzfc8FVHh+ITTv//Q9B6rN7O8tbOXsZHpEziUp8uob6mZOXOUVyBV16WLvjwwCWOj4wwPjM4orn76dCe7XoqXdxic9aHj2PTRtWGDiohIyRkfG6W/Zx99cZHV29VJf3cXvXGhdXhocM7npSurWLN+g/Y1iZS4JK4TdQ/wgJmdB/wK8NQv8W9IAqzMqG+uob65hhPPaAFg7+u9bPnbF5gYm6Cs/J11FrjydIpMY4pM4/RSifrmava81jt1Vrz2U5oCJhQRkVKVKk/TuLyNxuVtc/798MAAfd2dPPO97/LqE48C0UVuN136m5xz2eVJRhWRBCy6iHL3zfH9LjO7AHgfcLO7L26NlJSEFSc0cOn1G3UWuNjytfVcotdDRETepqpMhqrMiWy86CPsfPrJqYvctp+6PnQ0ESkCXWxXRERE5AjSRW5Fjj5JLOcTERERkXm0nXSyiieRY9zRf7YAERERERGRBKmIEhERERERKYCKKBERERERkQKoiBIRERERESmAiigREREREZECqIgSEREREREpgIooERERERGRAqiIEhERERERKYCKKBERERERkQKoiBIRERERESmAuXtxGzDLAq8VtZHFWQb0hA6BcuRTjplKIUcpZADlyKccMynHTKWQoxQygHLkU46ZlGOmUshRChkA3u3udYt9cHkxk8Rec/czE2hnQWb2jHIoh3IcHRmUQzmU4+jLUQoZlEM5lOPoy1EKGSZzFPJ4LecTEREREREpgIooERERERGRAiRRRN2dQBuLoRwzKcdMyjGtFDKAcuRTjpmUY6ZSyFEKGUA58inHTMoxUynkKIUMUGCOop9YQkRERERE5Fii5XwiIiIiIiIFUBH1DmRmS83sAjNbFjqLiMjRzsxWmNn5ZrboU+OKiMi0o3FsWtQiysy+YWZPmtmfFbOdRWZpNbPHA7Zfb2bfN7MfmNl3zawiUI5G4HvAJuARM2sOkSPO0mpmzwdsv9zM3jSzrfFtXagscZ47zOyjAdu/Jue1eMHM7gqUo9HMHjCzZ0JliHMcb2b/ZWaPm9nXAmWY6rfMLG1m95vZE2b2yVA54u9PNrMtSWbIz2Fm7fFn9YdmdreZWaAcJwH/DrwXeDSpvn2u32lmdqqZPZRE+3PlMLOVZrY7px9J7PfLPK/H/Wa2IakM+TnM7Nac1+JVM/t8oBxrzezhuF+/KakMc+Q43cz+J+7D/jih9meNvUKMTefJkfi4dI4cQcamc7TbSoCx6Xz//8WOT4tWRJnZZUDK3c8F1prZu4rV1iKyNAL3ALWhMgAfB2539wuBTuCiQDnWAze4+5eAB4HTA+UA+CpQHbD99cC97r45vm0PFcTMzgOWu/v9oTK4+52TrwXwOPAPgaJ8AvhmfM2IOjMLde2I24C/dPfzgFVmtjnJxufotz4LPOvu7wV+wxKa9cjPYWYnAH8N1CfR/nw5gKuBa9z9g8BqIJGDIHPkWA9c5e63Am8AxwfIQFxE3g6ki93+AjnOBr6U06fuC5QDM/s48Lq7v5BEhrlyuPstOX3qS8A/h8gBXAfc7O4bgF9LcICan+PrwFXA+4CPmVnRf1aYPfb6bcKMTfNz/A5hxqX5Oa4kzNg0P8dnCDM2nW9svqjxaTFnojYD346//gHRD00o48DlQH+oAO5+h7tPHiFsBroD5XjU3X9sZr9KVPE/GSKHmX0QGCT60IZyDvARM/tJfGQqiYtPz2JmaaKC5edmdkmIDHl5VgKt7l7QReeOoP3AqWbWQDQ47giU4yTgufjrbhIuGpjdb21muk99DEiquMzPkQU+llDb8+Zw9y+4+yvx3zWR3NXu83PcB+wysw8DjcDOpDPErgIeSaDthXKcA3zKzJ4zsy+HymFmS4GvAQfN7AOhckwys7OA3e6+J1CO/cD6+Gh/JdAbKMdSd+/w6Ixm+4ElxQ4wx9jr9wgwNp0jx88IMC6dI8dPQoxN58jxYIix6Vxj80LGp8UsomqByQ7jANBaxLYW5O797t4Xqv1cZnYu0OjuPw6YwYh+eA8CowHarwBuAm5Muu08TwPnu/smoqO3HwqU4wpgB/BXwCYz+2ygHJOuBe4M2P7/AmuAzwGvEPUfIdwH3GLREsuLgIeTbHyOfitIn5qfw9273f1wEm0vlGOSmV0OvOzubwXMkQF+C9gFFP2Ut/kZzKyJaHD41WK3vVAO4PtExf5ZwLlmtj5QjuuB7wB3AVeY2cWBckz6Q6JZmETMkeO/iQrczwE/BMYC5XjCzK4zs98FjgO2JZEDpsdeRAflgo1Nc8aAj4Ucl+aPRUONTXPbDTk2zfl8PEcB49NiFlEDTE+FZYrc1lEhPjr2dSDR/Qz5PHItUQeWyC+XPDcCd7h7UkfD5rPN3ffGXz8DhFpyuhG42907gX8FkjxyOoOZlcXtbw2VAbgF+Iy7/wXwKtER9sS5+xeJBoWfAu5x94EQOXKoT81jZmuBPwH+KGQOd+919yuJDsacFSDCV4DPu3viB8Xy/Mjds+4+DjxP2D717+M+9dtEhV0Q8Yx6i7u/HioD0e/c33f3LxD1IRcEynE1UZ9+HXCbJ3SNnbyxV7B+tFTGgPk5QuXKbzfU2DQvR0Hj02J+eJ5lepr0NODnRWyr5MWzL98h+kW3K2COPzWzK+JvG0huWj/X+cC1ZrYV2GBm/xggA8C/mNlpZpYCLgVeDJRjJ7A2/vpMoqPZoZwHPJXUL7d5NALr4vflbBI4sr+AF4B2or0moalPzRHvt7gX+GTgI7p3xktQIFyf+n7gtpw+9YsBMgA8aNGZCmuAC4n2AYVQSn3qJcADAduHaJ/eajOrItprEqRPjYvr1+Jvv5lEm3OMvYL0oyU0BpyRI1SuOXIEGZvO8f8vbHzq7kW5Ea11fZFo8PEKUF+stgrItDVg29cQTVFujW+XB8rRCDxEtKfiDuILLr9D35NTiY54bCfaDB0qR138Q/wY0TrglQGzfBm4LPBnYhPwMtERw4eATMAstwKfCPx6bI3v18Svy98RLUVNhcgx3/cBXo/bgL05fer7A+U4nmgJ6uPATSHfk1DvS85r8QGimYZtwHUBc7QRFS5PxH1IXaj3BfgWcHrSr0Xe6/FhopOeZIkOPATrO4hOpnBegm3nj72uDDE2nW8MmPTP6xw5bpkrV4jXgwBj0/nel8W+NxY/sCjiI4UXAI95NK0uIiK/JDNrIzqK+qCXyD5PEZGjicamcqQUtYgSERERERE51rzjNyaLiIiIiIgUQkWUiIiIiIhIAVREiYiIiIiIFEBFlIiIiIiISAFURImIiIiIiBTg/wGFXHkV1dR1+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# print(file[i])\n",
    "plt.figure(figsize=(12, 5))\n",
    "plt.title('观察数据')\n",
    "plt.tight_layout()\n",
    "ax = plt.gca()\n",
    "ax.set_xlim([0, SIZE])\n",
    "ax.xaxis.set_major_locator(MultipleLocator(1))  # 间距\n",
    "for j in filePrice:\n",
    "    plt.plot(j, marker=\".\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 第三部分：拆分训练集和测试集并采用支持向量机回归来进行预测  \n",
    "* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 603,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X = []\n",
    "y = []\n",
    "count  = 0\n",
    "for i in range(len(filePrice)):\n",
    "    if count == 0:\n",
    "        y = filePrice[i]\n",
    "        count += 1\n",
    "    else:\n",
    "        X.append(filePrice[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 643,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 605,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "X = np.array(X).T\n",
    "X = StandardScaler().fit_transform(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 606,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "y = np.array(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 607,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.svm import SVR\n",
    "from sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import warnings\n",
    "warnings.simplefilter(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 608,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xtrain, Xtest, Ytrain, Ytest = train_test_split(X,y, test_size=0.3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 609,
   "metadata": {},
   "outputs": [],
   "source": [
    "Kernel = [\"linear\", \"poly\", \"rbf\", \"sigmoid\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 610,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The accuracy under kernel linear is 0.713193\n",
      "The accuracy under kernel poly is -133.312343\n",
      "The accuracy under kernel rbf is -0.447896\n",
      "The accuracy under kernel sigmoid is 0.143410\n"
     ]
    }
   ],
   "source": [
    "for kernel in Kernel:\n",
    "    svr = SVR(kernel=kernel\n",
    "             ,gamma=\"auto\"\n",
    "             , degree =15\n",
    "             ,cache_size=50000\n",
    "             ).fit(Xtrain, Ytrain)\n",
    "    print(\"The accuracy under kernel %s is %f\" % (kernel, svr.score(Xtest, Ytest)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 611,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 确定核函数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 612,
   "metadata": {},
   "outputs": [],
   "source": [
    "svr = SVR(kernel=Kernel[0]  # 选择核函数\n",
    "         ,gamma=\"auto\"\n",
    "         ,degree =15\n",
    "         ,cache_size=50000\n",
    "         ).fit(Xtrain, Ytrain)\n",
    "\n",
    "y_predict=svr.predict(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 613,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([30.37358211, 34.02956125, 36.57492061, 38.85626127, 38.18738606,\n",
       "       35.19530284, 34.63030289, 30.53498188, 29.01629   , 28.59545819,\n",
       "       28.75939467, 27.73339936, 29.79530992, 30.10214007, 28.52319996,\n",
       "       27.13699826, 25.79499769, 23.04866241, 18.89041318, 17.03421772,\n",
       "       14.50030205, 15.29233303, 13.85737111, 17.79922199])"
      ]
     },
     "execution_count": 613,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_predict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 614,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r方： 0.7981664707349527\n",
      "均方误差MSE: 15.10156726730286\n",
      "平均绝对误差MAE: 3.1069662572533363\n"
     ]
    }
   ],
   "source": [
    "# print('svr模型自带评分机制：',svr.score(Xtest,y_predict))\n",
    "from sklearn.metrics import r2_score,mean_squared_error,mean_absolute_error\n",
    "print('r方：',r2_score(filePrice[0],y_predict))\n",
    "print('均方误差MSE:',mean_squared_error(filePrice[0],y_predict))\n",
    "print('平均绝对误差MAE:',mean_absolute_error(filePrice[0],y_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 615,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x178498813c8>]"
      ]
     },
     "execution_count": 615,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(y_predict)\n",
    "plt.plot(filePrice[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-----------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 683,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 设置预测数据\n",
    "Xpredict = []\n",
    "count = 0\n",
    "for i in range(len(filePrice)-1, -1, -1):\n",
    "    if count != 1:\n",
    "        Xpredict.append(filePrice[i])\n",
    "    count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 684,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print(Xpredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 685,
   "metadata": {},
   "outputs": [],
   "source": [
    "Xpredict = np.array(Xpredict).T\n",
    "Xpredict = StandardScaler().fit_transform(Xpredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 686,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "# Xpredict\n",
    "Ypredict = svr.predict(Xpredict)\n",
    "# Ypredict\n",
    "Ypredict = list(reversed(Ypredict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 687,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1784d858748>]"
      ]
     },
     "execution_count": 687,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(Ypredict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 688,
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.hstack((filePrice[0], Ypredict))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 最终验证效果数据和展示图"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 710,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 26.0, '月份时间轴')"
      ]
     },
     "execution_count": 710,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12, 5))\n",
    "plt.title('数据预测最终结果')\n",
    "plt.tight_layout()\n",
    "ax = plt.gca()\n",
    "ax.set_xlim([0, SIZE*2])\n",
    "ax.xaxis.set_major_locator(MultipleLocator(1))  # 间距\n",
    "plt.plot(filePrice[0])\n",
    "plt.plot(y_predict)\n",
    "plt.plot(a)\n",
    "plt.axvspan(xmin=23, xmax=48, facecolor='g', alpha=0.3)\n",
    "plt.annotate('预测起点202111',\n",
    "   xy=(23,17.79922199),\n",
    "   xytext=(23,25),\n",
    "   bbox = dict(boxstyle = 'round,pad=0.5',\n",
    "   fc = 'yellow', alpha = 0.5),\n",
    "   arrowprops=dict(facecolor='red',\n",
    "   shrink=0.05),fontsize=12)\n",
    "plt.annotate('预测终点202311',\n",
    "   xy=(47,25.79922199),\n",
    "   xytext=(47,30),\n",
    "   bbox = dict(boxstyle = 'round,pad=0.5',\n",
    "   fc = 'yellow', alpha = 0.5),\n",
    "   arrowprops=dict(facecolor='red',\n",
    "   shrink=0.05),fontsize=12)\n",
    "plt.ylabel('生猪价格')\n",
    "plt.xlabel('月份时间轴')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测前 24个月的实际数值：  \n",
    "【日期】：  \n",
    "[201912, 202001, 202002, 202003, 202004, 202005, 202006, 202007, 202008, 202009, 202010, 202011, 202012, 202101, 202102, 202103, 202104, 202105, 202106, 202107, 202108, 202109, 202110, 202111]  \n",
    "【数值】filePrice[0]：    \n",
    "[33.55, 36.64, 37.2, 35.03, 33.09, 29.23, 34.53, 37.36, 37.43, 33.74, 28.86, 29.82, 33.94, 34.64, 27.86, 26.69, 23.0, 18.56, 12.73, 15.61, 14.4, 12.2, 15.95, 17.15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测前24个月的预测数值：  \n",
    "【日期】：  \n",
    "[201912, 202001, 202002, 202003, 202004, 202005, 202006, 202007, 202008, 202009, 202010, 202011, 202012, 202101, 202102, 202103, 202104, 202105, 202106, 202107, 202108, 202109, 202110, 202111]  \n",
    "【数值】y_predict：    \n",
    "[30.37358211 34.02956125 36.57492061 38.85626127 38.18738606 35.19530284\n",
    " 34.63030289 30.53498188 29.01629    28.59545819 28.75939467 27.73339936\n",
    " 29.79530992 30.10214007 28.52319996 27.13699826 25.79499769 23.04866241\n",
    " 18.89041318 17.03421772 14.50030205 15.29233303 13.85737111 17.79922199]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测前24个的预测数值与实际数据的拟合效果：  \n",
    "**r方： 0.7981664707349527**  \n",
    "**均方误差MSE: 15.10156726730286**    \n",
    "**平均绝对误差MAE: 3.1069662572533363**    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* 预测后24个月的预测数据：   \n",
    "【日期】：  \n",
    "[202112~202311]  \n",
    "【数值】Ypredict：   \n",
    "[20.807477654891336, 17.449531077474877, 21.385698359855283, 21.984594927736666, 27.061093736883432, 28.718693564610053, 32.39847674608909, 32.044719456924135, 31.71550831866264, 31.479885643005712, 30.274148132962864, 29.58935227119936, 30.51897010466909, 31.042141880308044, 30.31763010747937, 27.98083103826386, 28.187611113101173, 26.522391400742034, 24.669928479168597, 26.064212385478957, 26.112974823733317, 26.157645380551596, 25.745117442625315, 26.033374475267486]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
